Triple
T9097479
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | L86 |
E218062
|
entity |
| Predicate | brand |
P1500
|
FINISHED |
| Object | GMC |
E11255
|
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: GMC | Statement: [L86, brand, GMC]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: GMC Context triple: [L86, brand, GMC]
-
A.
GMC
chosen
GMC is an American automotive marque of General Motors known for its trucks, SUVs, and commercial vehicles.
-
B.
GMC
GMC is the independent regulatory body that oversees medical education and professional standards for doctors in the United Kingdom.
-
C.
GMC
GMC is the commonly used abbreviation for the Great Midwest Conference, a former NCAA collegiate athletic conference in the United States.
-
D.
GMC
GMC is the abbreviation for the Common Market Group, the executive body responsible for coordinating and implementing policies within the Mercosur regional trade bloc.
-
E.
Chevrolet
Chevrolet is a major American automobile marque known for producing a wide range of affordable cars, trucks, and SUVs.
- 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_69ca83d9844081908e561e367fda6d45 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cc96b7d0d48190a3b15f35bef087e3 |
completed | April 1, 2026, 3:53 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d01798a79081909885a8e61bf04dc3 |
completed | April 3, 2026, 7:40 p.m. |
Created at: March 30, 2026, 7:15 p.m.