Triple
T2973130
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dub Taylor |
E80327
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Maverick |
E188689
|
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: [Dub Taylor, notableWork, Maverick]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maverick Context triple: [Dub Taylor, 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 classic American Western comedy television series that aired in the late 1950s, following the adventures of charming, poker-playing gambler Bret Maverick and his relatives.
-
D.
Brewster McCloud
Brewster McCloud is a 1970 surreal comedy film directed by Robert Altman about a reclusive young man living in the Houston Astrodome who dreams of building a pair of wings to fly.
-
E.
Doc Hudson
Doc Hudson is a wise, retired race car and town doctor in Pixar's "Cars" who mentors the protagonist Lightning McQueen.
- 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_69ad8b14ffe881908ffed62f9595c867 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9987bb6c8190adfb447b76276962 |
completed | March 8, 2026, 3:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b0fca910e481909c1d93b512a779aa |
completed | March 11, 2026, 5:24 a.m. |
Created at: March 8, 2026, 2:58 p.m.