Triple
T726059
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Orlyval |
E14727
|
entity |
| Predicate | servesAirportTerminal |
P6864
|
FINISHED |
| Object | Orly terminals 1, 2, 3 |
—
|
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: Orly terminals 1, 2, 3 | Statement: [Orlyval, servesAirportTerminal, Orly terminals 1, 2, 3]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: servesAirportTerminal Context triple: [Orlyval, servesAirportTerminal, Orly terminals 1, 2, 3]
-
A.
servesAirport
chosen
Indicates that a transportation service or route provides access to and operates for a particular airport.
-
B.
airportServesAs
Indicates that an airport functions in a particular role or capacity (such as primary, secondary, or hub) for a specified area, organization, or service.
-
C.
airportServed
Indicates that a particular airport provides service to, or is used for air travel to and from, a given location or area.
-
D.
hasPassengerTerminal
Indicates that one entity possesses or is equipped with a passenger terminal used for boarding, alighting, or handling passengers.
-
E.
hasCargoTerminal
Indicates that a location or facility includes or is equipped with a cargo terminal for handling freight.
- 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_69a4934c753c81909b309027e48b9b3a |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a64adf2c81908e48090be35dd9d9 |
completed | March 1, 2026, 8:49 p.m. |
| PD | Predicate disambiguation | batch_69a4a4f839608190878a60eb7a044ed9 |
completed | March 1, 2026, 8:43 p.m. |
Created at: March 1, 2026, 7:37 p.m.