Triple
T13154990
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Darla K. Anderson |
E312560
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Cars |
E46398
|
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: Cars | Statement: [Darla K. Anderson, notableWork, Cars]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cars Context triple: [Darla K. Anderson, notableWork, Cars]
-
A.
Cars
chosen
Cars is a 2006 Pixar animated film that follows a hotshot race car who discovers friendship and humility in a forgotten desert town.
-
B.
CAR
CAR is the Cordillera Administrative Region in the Philippines, an upland area in Northern Luzon known for its mountainous terrain and indigenous cultures.
-
C.
CAR
CAR is the commonly used abbreviation for Rugby Africa, the governing body for rugby union on the African continent.
-
D.
CAR
CAR is the standard NHL abbreviation for the Carolina Hurricanes professional ice hockey team.
-
E.
CAR
CAR is the commonly used abbreviation for the Chief of Army Reserve, the senior leader responsible for commanding and overseeing the United States Army Reserve.
- 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_69d806aabde48190899e13e41659cae5 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98c06ccb881909390df18e1a6f7ed |
completed | April 10, 2026, 11:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6eaee8aa0819089994b85d56c7740 |
completed | May 3, 2026, 6:27 a.m. |
Created at: April 9, 2026, 9:12 p.m.