Triple
T695171
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aéroport Charles de Gaulle 1 station |
E13878
|
entity |
| Predicate | distanceFromCentralParis |
P10703
|
FINISHED |
| Object | approximately 25 km |
—
|
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: approximately 25 km | Statement: [Aéroport Charles de Gaulle 1 station, distanceFromCentralParis, approximately 25 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromCentralParis Context triple: [Aéroport Charles de Gaulle 1 station, distanceFromCentralParis, approximately 25 km]
-
A.
distanceFromParisCenter
chosen
Indicates the measured distance between a given location and the central point of Paris.
-
B.
distanceFromCentralLondon
Indicates the spatial separation or length of travel between a given location and central London.
-
C.
distanceToGeneva
Indicates the spatial distance between a given entity and the location of Geneva.
-
D.
distanceFromCapital
Indicates the measured distance between a given location and the capital city of its corresponding region or country.
-
E.
distanceToBudapest_km
Indicates the physical distance, measured in kilometers, between a given location and Budapest.
- 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_69a493406c408190957eeec9048a8fb6 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a0c3f39c8190a3014df428817492 |
completed | March 1, 2026, 8:25 p.m. |
| PD | Predicate disambiguation | batch_69a49d23e0a08190b08be9d1eff2a1bb |
completed | March 1, 2026, 8:10 p.m. |
Created at: March 1, 2026, 7:36 p.m.