Triple
T568723
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TXL |
E13614
|
entity |
| Predicate | associatedWithAirportType |
P16113
|
FINISHED |
| Object | civil airport |
—
|
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: civil airport | Statement: [TXL, associatedWithAirportType, civil airport]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedWithAirportType Context triple: [TXL, associatedWithAirportType, civil airport]
-
A.
belongsToAirportSystem
Indicates that an airport is a member or component of a specific airport system or network.
-
B.
associatedWithIATAAirlineCode
Indicates that an entity is linked to or identified by a specific IATA airline code.
-
C.
hasAirportCodeType
Indicates that an airport code is associated with a specific classification or type (e.g., IATA, ICAO, FAA).
-
D.
airportRole
Indicates that an entity serves a specific functional role or capacity within the context of an airport.
-
E.
airlineType
Indicates the classification or category of an airline based on its operational or service characteristics.
- 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_69a4933fa4d88190a7949cc83c08c5c1 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49b0406d481908af5fc7bc67103fb |
completed | March 1, 2026, 8:01 p.m. |
| PD | Predicate disambiguation | batch_69a494c2caac819086ab316fa49d324c |
completed | March 1, 2026, 7:34 p.m. |
| PDg | Predicate description generation | batch_69a498dd579081908e02368a4c5efc8c |
completed | March 1, 2026, 7:51 p.m. |
Created at: March 1, 2026, 7:33 p.m.