Triple
T5589233
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NYPD Blue |
E146833
|
entity |
| Predicate | composer |
P1361
|
FINISHED |
| Object | Mike Post |
E268656
|
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: Mike Post | Statement: [NYPD Blue, composer, Mike Post]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mike Post Context triple: [NYPD Blue, composer, Mike Post]
-
A.
Mike Post
chosen
Mike Post is an American composer best known for creating iconic television theme music for series such as Law & Order, The A-Team, and NYPD Blue.
-
B.
Bill Conti
Bill Conti is an American composer and conductor best known for his iconic film and television scores, including the music for the Rocky series and various popular TV shows.
-
C.
Randy Edelman
Randy Edelman is an American composer best known for his prolific work on film and television scores, including numerous Hollywood action and drama movies.
-
D.
Nellee Hooper
Nellee Hooper is a British record producer and remixer known for his influential work with artists such as U2, Björk, Massive Attack, and Madonna.
-
E.
Ron Goodwin
Ron Goodwin was a British composer and conductor best known for his rousing film scores for war and adventure movies in the mid-20th century.
- 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_69c009036c408190981a8d690b679b67 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c0209ff5d88190843b6d134390ab71 |
completed | March 22, 2026, 5:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c04d342f7881908a79522692e8f7f9 |
completed | March 22, 2026, 8:12 p.m. |
Created at: March 22, 2026, 3:38 p.m.