Triple
T2179829
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Terminal 1 (Mexico City International Airport) |
E49013
|
entity |
| Predicate | numberOfMainTerminalsAtAirport |
P2957
|
FINISHED |
| Object | 2 |
—
|
LITERAL 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: 2 | Statement: [Terminal 1 (Mexico City International Airport), numberOfMainTerminalsAtAirport, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfMainTerminalsAtAirport Context triple: [Terminal 1 (Mexico City International Airport), numberOfMainTerminalsAtAirport, 2]
-
A.
airportHasTerminals
Indicates that a particular airport includes or is composed of one or more terminal facilities.
-
B.
numberOfTerminals
chosen
Indicates the total count of terminal points or endpoints associated with an entity.
-
C.
numberOfGates
Indicates the quantity of gates associated with or belonging to an entity.
-
D.
numberOfRunways
Indicates the quantity of runways associated with a given entity, such as an airport or airfield.
-
E.
hubAirport
Indicates that an airport serves as a primary hub or central operating base for a particular airline or carrier.
- F. None of above.
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_69a88aa72d348190a9544bb5b8a4e71d |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc4358fc88190a6f556c2de9fef8c |
completed | March 7, 2026, 6:22 a.m. |
| PD | Predicate disambiguation | batch_69abbda0ec948190be88c1243d81a423 |
completed | March 7, 2026, 5:54 a.m. |
Created at: March 4, 2026, 7:45 p.m.