Triple
T22037138
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michael Kahn |
E544240
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Jaws |
—
|
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: Jaws | Statement: [Michael Kahn, notableWork, Jaws]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jaws Context triple: [Michael Kahn, notableWork, Jaws]
-
A.
Jaws
Jaws is a 1974 thriller novel by Peter Benchley, best known for its story of a great white shark terrorizing a seaside resort town and inspiring the iconic 1975 film adaptation.
-
B.
Jaws
Jaws is the costumed jaguar mascot representing Indiana University–Purdue University Indianapolis (IUPUI) at its athletic events and campus activities.
-
C.
Jaws
chosen
Jaws is a 1975 American thriller film directed by Steven Spielberg about a great white shark that terrorizes a New England beach town.
-
D.
Jaws
Jaws is a towering, steel-toothed henchman and iconic villain from the James Bond film series, known for his near-invulnerability and menacing presence.
-
E.
Jaws
Jaws is the nickname of Darrell Waltrip, a famously outspoken and successful NASCAR driver and later television commentator.
- 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_69e11e2f98c8819083e11eab90942a78 |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f127f32edc81909b6898af6621f56f |
completed | April 28, 2026, 9:34 p.m. |
Created at: April 16, 2026, 8:25 p.m.