Triple
T514232
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BMW |
E10671
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object | BMW |
E10671
|
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: BMW | Statement: [BMW, shortName, BMW]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: BMW Context triple: [BMW, shortName, BMW]
-
A.
BMW
chosen
BMW is a German luxury automobile and motorcycle manufacturer renowned for its performance-oriented vehicles and engineering.
-
B.
Audi
Audi is a German luxury automobile manufacturer known for its premium vehicles, advanced engineering, and signature quattro all-wheel-drive technology.
-
C.
Škoda
Škoda is a Czech automobile manufacturer known for producing practical, affordable cars and operating as a subsidiary brand within the Volkswagen Group.
-
D.
Porsche
Porsche is a German luxury automobile manufacturer renowned for its high-performance sports cars, SUVs, and engineering excellence.
-
E.
Mercedes-Benz
Mercedes-Benz is a German luxury automobile manufacturer renowned for its premium cars, engineering innovation, and iconic three-pointed star logo.
- 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_69a2e84a0d08819087e01863fcd9abf1 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f1824a248190808591ab3ece7dff |
completed | Feb. 28, 2026, 1:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a4a42a4a648190a1a01d823b698ae3 |
completed | March 1, 2026, 8:40 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.