Triple
T15121323
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sorry |
E361178
|
entity |
| Predicate | writer |
P1360
|
FINISHED |
| Object |
Michael Tucker
Michael Tucker is an American television writer known for his work on the sitcom "Sorry."
|
E1158462
|
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 Tucker | Statement: [Sorry, writer, Michael Tucker]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michael Tucker Context triple: [Sorry, writer, Michael Tucker]
-
A.
Michael Tucker
Michael Tucker, better known by his stage name BloodPop, is an American musician and record producer recognized for his work on numerous pop hits.
-
B.
Michael Tucker
Michael Tucker is an American actor best known for his work in film, television, and theater, including roles in projects like the Woody Allen film "Radio Days" and the TV series "L.A. Law."
-
C.
Duncan Tucker
Duncan Tucker is an American film director and screenwriter best known for writing and directing the independent drama "Transamerica."
-
D.
Kevin Mullen
Kevin Mullen is a personal name shared by multiple individuals, including professionals in fields such as sports, academia, and public service.
-
E.
Mark Tucker
Mark Tucker is a video game designer known for his work on Bethesda's online action role-playing game Fallout 76.
- 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 Tucker Triple: [Sorry, writer, Michael Tucker]
Generated description
Michael Tucker is an American television writer known for his work on the sitcom "Sorry."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Michael Tucker Target entity description: Michael Tucker is an American television writer known for his work on the sitcom "Sorry."
-
A.
Michael Tucker
Michael Tucker is an American actor best known for his work in film, television, and theater, including roles in projects like the Woody Allen film "Radio Days" and the TV series "L.A. Law."
-
B.
Michael Tucker
Michael Tucker, better known by his stage name BloodPop, is an American musician and record producer recognized for his work on numerous pop hits.
-
C.
Duncan Tucker
Duncan Tucker is an American film director and screenwriter best known for writing and directing the independent drama "Transamerica."
-
D.
Kevin Mullen
Kevin Mullen is a personal name shared by multiple individuals, including professionals in fields such as sports, academia, and public service.
-
E.
Mark Tucker
Mark Tucker is a video game designer known for his work on Bethesda's online action role-playing game Fallout 76.
- 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_69d85a06450081909c5a14ea9851a15e |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0059f69a881909929a037a0eef702 |
completed | April 15, 2026, 9:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff2ce5d0708190bbfff5d68c5e7a3c |
completed | May 9, 2026, 12:47 p.m. |
| NEDg | Description generation | batch_69ff2d991c94819094e00a634d5b0948 |
completed | May 9, 2026, 12:50 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff2e5077cc819096208137ef5c99bb |
completed | May 9, 2026, 12:53 p.m. |
Created at: April 10, 2026, 3:06 a.m.