Triple
T19174561
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Terminal A at Dallas/Fort Worth International Airport |
E469406
|
entity |
| Predicate | hasAirlineCheckInCounters |
P24791
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Terminal A at Dallas/Fort Worth International Airport, hasAirlineCheckInCounters, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAirlineCheckInCounters Context triple: [Terminal A at Dallas/Fort Worth International Airport, hasAirlineCheckInCounters, yes]
-
A.
hasNumberOfPassengerTerminalsAtAirport
Indicates the relationship that specifies how many passenger terminals are present at a given airport.
-
B.
hasCheckInCounters
chosen
Indicates that an entity is associated with one or more check-in counters used for processing arrivals or registrations.
-
C.
hasPassengerBoardingGates
Indicates that an entity is associated with or contains one or more passenger boarding gates used for embarking or disembarking passengers.
-
D.
hasBoardingGatesFor
Indicates that a location or facility provides designated boarding gates used for embarking passengers onto specific transportation services (such as flights or trains).
-
E.
hasRunwayCount
Indicates the number of runways that a given entity (such as an airport) possesses.
- 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_69d8dd09d5a081909ae43c286651ae5a |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5f166d3888190adaf6dc8531a8ed1 |
completed | April 20, 2026, 9:27 a.m. |
| PD | Predicate disambiguation | batch_69e4b9b83d6881908e6271c620f74100 |
completed | April 19, 2026, 11:17 a.m. |
Created at: April 10, 2026, 12:06 p.m.