Triple
T14653696
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Destroyer |
E344054
|
entity |
| Predicate | musicBy |
P1952
|
FINISHED |
| Object | Theodore Shapiro |
—
|
NE NERFINISHED |
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: Theodore Shapiro | Statement: [Destroyer, musicBy, Theodore Shapiro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Theodore Shapiro Context triple: [Destroyer, musicBy, Theodore Shapiro]
-
A.
Theodore Shapiro
chosen
Theodore Shapiro is an American film composer known for his work on numerous Hollywood comedies and dramas, including scores for major studio films.
-
B.
Irving Shapiro
Irving Shapiro was an influential American corporate executive and lawyer best known for leading DuPont and helping to shape modern U.S. business policy and corporate governance.
-
C.
George Shapiro
George Shapiro was a prominent American talent manager and television producer best known for managing Jerry Seinfeld and producing the hit sitcom "Seinfeld."
-
D.
Stanley Shapiro
Stanley Shapiro was an American screenwriter best known for his sharp comedic scripts in mid-20th-century Hollywood, including several hit romantic comedies.
-
E.
Donald P. Greenberg
Donald P. Greenberg is a pioneering computer graphics researcher and educator known for his influential work in rendering, visualization, and the development of computer graphics programs at Cornell University.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d822e1a2cc81908e5bb93cf61ce3cc |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb518f7dc8190877997ea4cd3eed2 |
completed | April 14, 2026, 9:43 p.m. |
Created at: April 10, 2026, 1:27 a.m.