Triple
T7924751
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | British Leyland |
E184031
|
entity |
| Predicate | ownedBrand |
P1500
|
FINISHED |
| Object | MG |
E91030
|
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 | Statement: [British Leyland, ownedBrand, MG]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MG Context triple: [British Leyland, ownedBrand, MG]
-
A.
MG
chosen
MG is a historic British automotive marque best known for its sports cars, now owned and produced by Chinese manufacturer SAIC Motor.
-
B.
MGY
MGY was the distinctive wireless call sign used by the RMS Titanic for its radio communications.
-
C.
Marg
Marg is a given name, typically a shortened form of Margaret, used primarily in English-speaking contexts.
-
D.
MGN
MGN is the FAA airport code for Harbor Springs Municipal Airport, a public-use airfield serving Harbor Springs, Michigan.
-
E.
MZG
MZG is the vehicle registration code used on license plates for vehicles registered in the town of Wadern in Germany.
- 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_69ca828fe7bc819090f52c88dcd72183 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3aad48908190911905b4635bf01e |
completed | March 31, 2026, 3:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cb5bf89a0c81908e1cb988c0bc1d62 |
completed | March 31, 2026, 5:30 a.m. |
Created at: March 30, 2026, 5:06 p.m.