Triple
T14567191
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amour |
E341814
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object |
Michael Katz
Michael Katz is a film producer best known for his work on acclaimed European art-house and independent films.
|
E1236035
|
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: Michael Katz | Statement: [Amour, producer, Michael Katz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michael Katz Context triple: [Amour, producer, Michael Katz]
-
A.
Daniel Katz
Daniel Katz is an environmental activist and social entrepreneur best known for co-founding the Rainforest Alliance, a leading international conservation and sustainability organization.
-
B.
Daniel Katz
Daniel Katz is a cinematographer known for his work on the darkly comedic horror film "Come to Daddy."
-
C.
Don Katz
Don Katz is an American entrepreneur and author best known as the founder of the audiobook and spoken-word entertainment company Audible.
-
D.
Stephen Katz
Stephen Katz is a humorous, bumbling yet loyal companion character in Bill Bryson’s travel memoir "A Walk in the Woods," known for his comic misadventures on the Appalachian Trail.
-
E.
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."
- 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: Michael Katz Triple: [Amour, producer, Michael Katz]
Generated description
Michael Katz is a film producer best known for his work on acclaimed European art-house and independent films.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Michael Katz Target entity description: Michael Katz is a film producer best known for his work on acclaimed European art-house and independent films.
-
A.
Daniel Katz
Daniel Katz is an environmental activist and social entrepreneur best known for co-founding the Rainforest Alliance, a leading international conservation and sustainability organization.
-
B.
Daniel Katz
Daniel Katz is a cinematographer known for his work on the darkly comedic horror film "Come to Daddy."
-
C.
Don Katz
Don Katz is an American entrepreneur and author best known as the founder of the audiobook and spoken-word entertainment company Audible.
-
D.
Stephen Katz
Stephen Katz is a humorous, bumbling yet loyal companion character in Bill Bryson’s travel memoir "A Walk in the Woods," known for his comic misadventures on the Appalachian Trail.
-
E.
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."
- 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_69d822dcc6248190bed689984bceb0e2 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb38d89fc819086709fd3607b835f |
completed | April 14, 2026, 9:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00baf8a2648190adf3ad3af118187f |
completed | May 10, 2026, 5:06 p.m. |
| NEDg | Description generation | batch_6a00bbb628208190b0be62a333e7e442 |
completed | May 10, 2026, 5:09 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a00bc3a4b888190bd190b9330e2777d |
completed | May 10, 2026, 5:11 p.m. |
Created at: April 10, 2026, 1:23 a.m.