Triple
T4441288
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pumping Iron |
E95775
|
entity |
| Predicate | follows |
P134
|
FINISHED |
| Object |
Mike Katz
Mike Katz is an American bodybuilder and former professional football player best known for his appearance in the 1977 documentary film "Pumping Iron."
|
E439277
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Mike Katz | Statement: [Pumping Iron, follows, Mike Katz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mike Katz Context triple: [Pumping Iron, follows, Mike Katz]
-
A.
Jason Katz
Jason Katz is an American screenwriter and story artist best known for his work on Pixar animated films.
-
B.
Don Katz
Don Katz is an American entrepreneur and author best known as the founder of the audiobook and spoken-word entertainment company Audible.
-
C.
Michael Kagan
Michael Kagan is an Israeli technologist and entrepreneur best known as the co-founder and longtime chief technology officer of high-performance networking company Mellanox Technologies.
-
D.
Michael Rachmil
Michael Rachmil is a film producer best known for his work on the 1987 romantic comedy "Roxanne" starring Steve Martin.
-
E.
Mitch Kertzman
Mitch Kertzman is an American technology executive and entrepreneur best known for his leadership roles in the software and semiconductor industries, including at companies like LSI Logic and Sybase.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Mike Katz Triple: [Pumping Iron, follows, Mike Katz]
Generated description
Mike Katz is an American bodybuilder and former professional football player best known for his appearance in the 1977 documentary film "Pumping Iron."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mike Katz Target entity description: Mike Katz is an American bodybuilder and former professional football player best known for his appearance in the 1977 documentary film "Pumping Iron."
-
A.
Jason Katz
Jason Katz is an American screenwriter and story artist best known for his work on Pixar animated films.
-
B.
Don Katz
Don Katz is an American entrepreneur and author best known as the founder of the audiobook and spoken-word entertainment company Audible.
-
C.
Michael Kagan
Michael Kagan is an Israeli technologist and entrepreneur best known as the co-founder and longtime chief technology officer of high-performance networking company Mellanox Technologies.
-
D.
Michael Rachmil
Michael Rachmil is a film producer best known for his work on the 1987 romantic comedy "Roxanne" starring Steve Martin.
-
E.
Mitch Kertzman
Mitch Kertzman is an American technology executive and entrepreneur best known for his leadership roles in the software and semiconductor industries, including at companies like LSI Logic and Sybase.
- F. None of above. chosen
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69b3453ea2b48190a26f154b3b8fece5 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b355ad71588190b1dcad4250472c29 |
completed | March 13, 2026, 12:09 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b61380fca08190bf036a7d82cee0e7 |
completed | March 15, 2026, 2:03 a.m. |
| NEDg | Description generation | batch_69b6143adefc81908f6c639906e0fd1a |
completed | March 15, 2026, 2:06 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b614b00dbc8190a7ea0477bedbc96a |
completed | March 15, 2026, 2:08 a.m. |
Created at: March 12, 2026, 11:32 p.m.