Triple
T11055349
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MMMY |
E261359
|
entity |
| Predicate | hasPassengerTerminal |
P1297
|
FINISHED |
| Object | Terminal B |
E261362
|
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: Terminal B | Statement: [MMMY, hasPassengerTerminal, Terminal B]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Terminal B Context triple: [MMMY, hasPassengerTerminal, Terminal B]
-
A.
Terminal B
Terminal B is one of the passenger terminals at Vnukovo International Airport in Moscow, serving as a key facility for handling flights and travelers.
-
B.
Terminal B
Terminal B is one of the passenger terminals at Sheremetyevo International Airport in Moscow, serving as a hub for domestic and selected international flights.
-
C.
Terminal B
Terminal B is a passenger terminal at San Jose International Airport serving commercial airline flights and travelers in San Jose, California.
-
D.
Terminal B
Terminal B is one of the main passenger terminals at Boston Logan International Airport, serving numerous domestic and some international flights with multiple airlines.
-
E.
Terminal B
chosen
Terminal B is one of the passenger terminals at General Mariano Escobedo International Airport in Monterrey, Mexico, serving commercial airline operations and traveler 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_69d6aa98650481908609c7c56bfa7902 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d798a152b4819095b74a8996346077 |
completed | April 9, 2026, 12:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e3c86e0e6481908f091497313132c1 |
completed | April 18, 2026, 6:07 p.m. |
Created at: April 8, 2026, 9:26 p.m.