Triple
T4526314
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Montpellier-Méditerranée Airport |
E106185
|
entity |
| Predicate | runway12R_30Lsurface |
P422
|
FINISHED |
| Object | asphalt |
—
|
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: asphalt | Statement: [Montpellier-Méditerranée Airport, runway12R_30Lsurface, asphalt]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: runway12R_30Lsurface Context triple: [Montpellier-Méditerranée Airport, runway12R_30Lsurface, asphalt]
-
A.
runwaySurface
chosen
Indicates the type or condition of the surface material that a runway is made of or covered with.
-
B.
runway12_30Usage
Indicates the use or operational status of runway 12/30 for aircraft movements or related airport activities.
-
C.
runwayLength
Indicates the length of a runway associated with an airport or airfield.
-
D.
runwayWidth
Indicates the measured width of a runway as a spatial dimension.
-
E.
runwayPerformance
Indicates the performance characteristics or behavior of an entity (such as an aircraft or vehicle) when operating on a runway, including factors like acceleration, deceleration, and required distances.
- 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_69bd43f3d6e08190a91824f833d51bbe |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd57760f4481908f69ce82be63d7f8 |
completed | March 20, 2026, 2:19 p.m. |
| PD | Predicate disambiguation | batch_69bd521cf77c819083852de3094d1377 |
completed | March 20, 2026, 1:56 p.m. |
Created at: March 20, 2026, 1:03 p.m.