Triple
T4035620
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gore Verbinski |
E83820
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Gore |
E148999
|
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: Gore | Statement: [Gore Verbinski, givenName, Gore]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gore Context triple: [Gore Verbinski, givenName, Gore]
-
A.
Gore
chosen
Gore is a surname most prominently associated with Albert Gore Jr., better known as Al Gore, the former U.S. Vice President and environmental advocate.
-
B.
Slaughter
Slaughter is the surname of Louise Slaughter, a long-serving American congresswoman known for her work on health care, ethics, and women's rights.
-
C.
Savage
Savage is the surname of Nigerian singer, songwriter, and actress Tiwa Savage, a prominent figure in contemporary Afrobeats music.
-
D.
Great Kills
Great Kills is a residential neighborhood on Staten Island’s South Shore known for its marina, waterfront parks, and suburban character.
-
E.
Bloods
Bloods is a popular nickname for the Sydney Swans, an Australian Football League club known for its red-and-white colors and strong team culture.
- 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_69aed92f7cf0819098e0539bdcc3767f |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefb132f6c8190937acd35a6a5a9e4 |
completed | March 9, 2026, 4:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b556415ebc8190a528c7e22dbf70df |
completed | March 14, 2026, 12:36 p.m. |
Created at: March 9, 2026, 3:36 p.m.