Triple
T12287413
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mont Perdu |
E292864
|
entity |
| Predicate | nearestMajorTownFrance |
P104062
|
FINISHED |
| Object | Gavarnie vicinity |
—
|
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: Gavarnie vicinity | Statement: [Mont Perdu, nearestMajorTownFrance, Gavarnie vicinity]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nearestMajorTownFrance Context triple: [Mont Perdu, nearestMajorTownFrance, Gavarnie vicinity]
-
A.
nearestMajorCity
Indicates that one city is the closest significant urban center to another location or city compared to all other major cities.
-
B.
hasCommunesNear
Indicates that one entity has communes located in its nearby geographic vicinity.
-
C.
capitalOfProvinceNearby
Indicates that a city serves as the capital of a province that is geographically close to a specified reference location or area.
-
D.
distanceFromCalais
Indicates the measured distance separating a given place or object from the location of Calais.
-
E.
hasNeighboringFrenchCommune
Indicates that one commune is geographically adjacent to another commune located in France.
- F. None of above. chosen
Provenance (4 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_69d6ab690ad081908c0ed3870ec82d53 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d9261e1570819084bb4fdb44aa6aea |
completed | April 10, 2026, 4:32 p.m. |
| PD | Predicate disambiguation | batch_69d91c4d9a9c8190aeb7beaf9792d8f0 |
completed | April 10, 2026, 3:50 p.m. |
| PDg | Predicate description generation | batch_69d9261b7f088190b69fe6961015fce3 |
completed | April 10, 2026, 4:32 p.m. |
Created at: April 8, 2026, 9:52 p.m.