Triple
T22279440
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LIS Terminal 2 |
E550691
|
entity |
| Predicate | relativePositionAtAirport |
P72545
|
FINISHED |
| Object | secondary terminal |
—
|
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: secondary terminal | Statement: [LIS Terminal 2, relativePositionAtAirport, secondary terminal]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relativePositionAtAirport Context triple: [LIS Terminal 2, relativePositionAtAirport, secondary terminal]
-
A.
hasRelativePositionAtAirport
chosen
Indicates that one entity has a specific spatial or positional relationship to another entity within the context or layout of an airport.
-
B.
nearbyAirportTerminal
Indicates that one airport terminal is located close to another airport terminal in physical space.
-
C.
nearbyAirportRelationship
Indicates that one location has an airport situated close enough to serve it conveniently, establishing a nearby-airport relationship between the two.
-
D.
roleComparedToNearbyAirport
Indicates how the functional importance or role of an airport compares relative to other nearby airports.
-
E.
locatedAtAirportCode
Indicates that an entity is situated at, associated with, or occurs at the airport identified by a specific airport code.
- 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_69e11e44d538819097c6b8f333af3352 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f14eaa8cec819081c2ad031154ebe7 |
completed | April 29, 2026, 12:19 a.m. |
| PD | Predicate disambiguation | batch_69e72ff0363081909f794d19c8a64837 |
completed | April 21, 2026, 8:06 a.m. |
Created at: April 16, 2026, 8:40 p.m.