Triple
T525363
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Le Mesnil-Amelot |
E10904
|
entity |
| Predicate | hasAirportInfrastructure |
P13763
|
FINISHED |
| Object | Paris Charles de Gaulle Airport facilities |
—
|
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: Paris Charles de Gaulle Airport facilities | Statement: [Le Mesnil-Amelot, hasAirportInfrastructure, Paris Charles de Gaulle Airport facilities]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAirportInfrastructure Context triple: [Le Mesnil-Amelot, hasAirportInfrastructure, Paris Charles de Gaulle Airport facilities]
-
A.
hasInternationalAirport
Indicates that a place possesses an airport that handles international flights and services cross-border air traffic.
-
B.
containsAirfield
Indicates that a location or area includes at least one airfield within its boundaries.
-
C.
hasMajorAirport
Indicates that a location possesses at least one significant airport that serves as a primary hub for air travel in that area.
-
D.
hasAirportClassification
Indicates that an airport is assigned a specific classification or category based on defined criteria.
-
E.
aircraftFacility
chosen
Indicates that a facility is designed, equipped, or used to support the operation, maintenance, or accommodation of aircraft.
- 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_69a2e84b16c4819088d284c47c3a7968 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f1b7f448819087e5e7f3b37d7142 |
completed | Feb. 28, 2026, 1:46 p.m. |
| PD | Predicate disambiguation | batch_69a2f0198ecc8190883849e5a8245963 |
completed | Feb. 28, 2026, 1:39 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.