Triple
T16985694
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Patricia Breslin |
E412058
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Maverick |
E517707
|
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: Maverick | Statement: [Patricia Breslin, notableWork, Maverick]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maverick Context triple: [Patricia Breslin, notableWork, Maverick]
-
A.
Maverick
Maverick is an MBTA subway station on Boston’s Blue Line serving the East Boston neighborhood.
-
B.
Maverick
Maverick is a political nickname for U.S. Senator John McCain, reflecting his reputation for independence and willingness to break with his party.
-
C.
Maverick
chosen
Maverick is a 1994 comedic Western film starring Mel Gibson, Jodie Foster, and James Garner, centered on a charming gambler trying to raise money for a high-stakes poker tournament.
-
D.
Maverick
Maverick is a cigarette brand known for its budget-friendly positioning within the U.S. tobacco market.
-
E.
Maverick
Maverick is a surname of English origin borne by various individuals, including those with the given name Moses Maverick.
- 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_69d886ca8f348190812768ea8d5055ce |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d18af95c8190a25ef0614e1a17f3 |
completed | April 18, 2026, 6:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00dc1109a081908890bbd5958c76c2 |
completed | May 10, 2026, 7:27 p.m. |
Created at: April 10, 2026, 5:32 a.m.