Triple
T11343396
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mike Post |
E268656
|
entity |
| Predicate | name |
P16
|
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: [Mike Post, name, Mike Post]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mike Post Context triple: [Mike Post, name, 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.
Don Grusin
Don Grusin is an American jazz and fusion keyboardist, composer, and producer known for his solo work and collaborations within contemporary jazz, including projects with his brother Dave Grusin.
-
C.
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.
-
D.
Mack Gordon
Mack Gordon was an American lyricist and songwriter renowned for crafting numerous popular standards for film and stage during the 1930s and 1940s.
-
E.
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.
- 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_69d6aacb1f0881908c84a349fd1be047 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7ea1e360c8190a02d1e2d1d6f4b5d |
completed | April 9, 2026, 6:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e5435f4e288190b9b0029dc35bf2c5 |
completed | April 19, 2026, 9:04 p.m. |
Created at: April 8, 2026, 9:33 p.m.