Triple
T16179193
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MG Cars |
E392642
|
entity |
| Predicate | notableModel |
P1503
|
FINISHED |
| Object | MG Metro |
E392653
|
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: MG Metro | Statement: [MG Cars, notableModel, MG Metro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MG Metro Context triple: [MG Cars, notableModel, MG Metro]
-
A.
MG Metro
chosen
The MG Metro is a performance-oriented small hatchback produced by MG in the 1980s as a sportier, tuned version of the Austin/Rover Metro.
-
B.
Metros
Metros is the nickname historically used for the MetroStars, the former Major League Soccer team now known as the New York Red Bulls.
-
C.
Metro
"Metro" is a Russian disaster thriller film featuring Svetlana Khodchenkova in a prominent role, centered on a catastrophic flood in the Moscow subway system.
-
D.
Metro
Metro is the rapid transit system serving the Washington, D.C. metropolitan area, operated by the Washington Metropolitan Area Transit Authority (WMATA).
-
E.
Metro
Metro is the public transportation agency serving the St. Louis metropolitan area, operating bus, light rail, and paratransit services.
- 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_69d87f1d32208190942e4e499a80c18c |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e2205b88b481908ecdd8d663dc668b |
completed | April 17, 2026, 11:58 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fffefe4dc08190a6cc43a448ae6554 |
completed | May 10, 2026, 3:43 a.m. |
Created at: April 10, 2026, 5:02 a.m.