Triple
T856590
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Terminal 3 (Paris Charles de Gaulle Airport) |
E18505
|
entity |
| Predicate | hasRunwayAccessAt |
P18604
|
FINISHED |
| Object | Paris Charles de Gaulle Airport runways |
—
|
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 runways | Statement: [Terminal 3 (Paris Charles de Gaulle Airport), hasRunwayAccessAt, Paris Charles de Gaulle Airport runways]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRunwayAccessAt Context triple: [Terminal 3 (Paris Charles de Gaulle Airport), hasRunwayAccessAt, Paris Charles de Gaulle Airport runways]
-
A.
hasRunwayAccessVia
chosen
Indicates that an entity has access to a runway by means of a specified connecting route, facility, or intermediary.
-
B.
hasRunwayUse
Indicates that a particular runway is authorized or designated for use by a specific aircraft, operation, or purpose.
-
C.
hasRunwayType
Indicates that an airport or airfield has a runway of a specified type or surface classification.
-
D.
hasRunwayNumber
Indicates that an airport or airfield runway is assigned a specific identifying number.
-
E.
hasRunwayCount
Indicates the number of runways that a given entity (such as an airport) possesses.
- 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_69a4938bdd3c8190a954a3c11844d9cf |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4ac3c172481908ed164ee1579ec28 |
completed | March 1, 2026, 9:14 p.m. |
| PD | Predicate disambiguation | batch_69a4aa834a588190bca4a0eb83fb3eb6 |
completed | March 1, 2026, 9:07 p.m. |
Created at: March 1, 2026, 7:39 p.m.