Triple
T13235515
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ross Hunter |
E315137
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Ross Hunter |
E315137
|
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: Ross Hunter | Statement: [Ross Hunter, name, Ross Hunter]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ross Hunter Context triple: [Ross Hunter, name, Ross Hunter]
-
A.
Ross Hunter
chosen
Ross Hunter was a prominent American film producer best known for his lavish, emotionally charged Hollywood melodramas of the 1950s and 1960s.
-
B.
George Hunter
George Hunter was a prominent Chattanooga businessman and philanthropist whose legacy includes the endowment that led to the creation of the Hunter Museum of American Art.
-
C.
Will Gardner
Will Gardner is a charismatic and ambitious lawyer and name partner at the Chicago law firm Lockhart/Gardner in the television drama "The Good Wife."
-
D.
Alex Reiger
Alex Reiger is the level-headed, philosophical cab driver who serves as the central character in the classic television sitcom "Taxi."
-
E.
Jonathan Hunter
Jonathan Hunter is the son of British screenwriter Ian McLellan Hunter, who was known for his work in mid-20th-century cinema.
- 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_69d806affc688190a25b6ccc588e9c72 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98d36bdf8819099949b1e0e6902d3 |
completed | April 10, 2026, 11:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6ff3079c08190977663e5d4762a80 |
completed | May 3, 2026, 7:54 a.m. |
Created at: April 9, 2026, 9:22 p.m.