Triple
T9873096
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sally Carrera |
E240004
|
entity |
| Predicate | helpsCharacter |
P7748
|
FINISHED |
| Object | Lightning McQueen |
E236507
|
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: Lightning McQueen | Statement: [Sally Carrera, helpsCharacter, Lightning McQueen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lightning McQueen Context triple: [Sally Carrera, helpsCharacter, Lightning McQueen]
-
A.
Lightning McQueen
chosen
Lightning McQueen is a hotshot red race car and the ambitious, charismatic protagonist of Pixar's animated Cars film series.
-
B.
Tia (Cars character)
Tia is a minor character in Pixar's "Cars," portrayed as an enthusiastic yellow Mazda Miata fangirl of Lightning McQueen who appears alongside her twin sister Mia.
-
C.
Doc Hudson
Doc Hudson is a wise, retired race car and town doctor in Pixar's "Cars" who mentors the protagonist Lightning McQueen.
-
D.
BURN-E
BURN-E is a 2008 Pixar animated short film, spun off from the movie WALL·E, that follows a repair robot whose mission is repeatedly disrupted by the main film’s events.
-
E.
Woody
Woody is the commonly used nickname of American businessman and New York Jets owner Woody Johnson.
- 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_69ca84e8a0788190b9061811d50fd554 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb3f754008190abe3fe034b42908e |
completed | April 2, 2026, 12:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d23d09790c8190be161d2dbef2e881 |
completed | April 5, 2026, 10:44 a.m. |
Created at: March 30, 2026, 8:37 p.m.