Triple
T885172
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dallas Love Field |
E19113
|
entity |
| Predicate | numberOfGates |
P21387
|
FINISHED |
| Object | 20 gates (approximate, post-redevelopment) |
—
|
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: 20 gates (approximate, post-redevelopment) | Statement: [Dallas Love Field, numberOfGates, 20 gates (approximate, post-redevelopment)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfGates Context triple: [Dallas Love Field, numberOfGates, 20 gates (approximate, post-redevelopment)]
-
A.
hasBoardingGatesFor
Indicates that a location or facility provides designated boarding gates used for embarking passengers onto specific transportation services (such as flights or trains).
-
B.
numberOfRunways
Indicates the quantity of runways associated with a given entity, such as an airport or airfield.
-
C.
hasNumberOfEntrances
Indicates the relationship that specifies how many entrances an entity possesses.
-
D.
numberOfTerminals
Indicates the total count of terminal points or endpoints associated with an entity.
-
E.
numberOfCorridors
Indicates the total count of corridors associated with or contained within a given entity or structure.
- F. None of above. chosen
Provenance (4 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_69a4939c32488190a7ccd41cf0abb22b |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4ae787bf081909533082ca013624a |
completed | March 1, 2026, 9:24 p.m. |
| PD | Predicate disambiguation | batch_69a4aa8ff8c48190a33b00acf65c1276 |
completed | March 1, 2026, 9:07 p.m. |
| PDg | Predicate description generation | batch_69a4ae774fac8190b3134d64086d65fe |
completed | March 1, 2026, 9:24 p.m. |
Created at: March 1, 2026, 7:39 p.m.