Triple
T13834960
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Why Did I Get Married Too? |
E332501
|
entity |
| Predicate | musicBy |
P1952
|
FINISHED |
| Object | Aaron Zigman |
E118675
|
NE FINISHED |
How this triple was built (2 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: Aaron Zigman | Statement: [Why Did I Get Married Too?, musicBy, Aaron Zigman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aaron Zigman Context triple: [Why Did I Get Married Too?, musicBy, Aaron Zigman]
-
A.
Aaron Zigman
chosen
Aaron Zigman is an American composer and producer best known for his film scores on dramas and romantic films such as "The Notebook" and "John Q."
-
B.
Scott Gilman
Scott Gilman is a musician best known as a member of the British-American rock band Foreigner.
-
C.
Sam Koppelman
Sam Koppelman is an American writer and political speechwriter known for co-authoring books with figures like Beto O’Rourke and for his work on voting rights and democracy.
-
D.
Mitchell Hurwitz
Mitchell Hurwitz is an American television writer and producer best known for creating the critically acclaimed sitcom "Arrested Development."
-
E.
Lee Eisenberg
Lee Eisenberg is an American television writer and producer best known for his work on series such as The Office and Hello Ladies.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d81c5ae7c88190b0dd41bdafeb5999 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de029b352081909605baaedc336213 |
completed | April 14, 2026, 9:02 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7c0ed7e8c81909ffed37f5b097188 |
completed | May 3, 2026, 9:41 p.m. |
Created at: April 9, 2026, 10:13 p.m.