Triple
T15409515
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Collonges |
E368546
|
entity |
| Predicate | distanceToGenevaKilometers |
P15630
|
FINISHED |
| Object | about 15 |
—
|
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: about 15 | Statement: [Collonges, distanceToGenevaKilometers, about 15]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToGenevaKilometers Context triple: [Collonges, distanceToGenevaKilometers, about 15]
-
A.
distanceToGeneva
chosen
Indicates the spatial distance between a given entity and the location of Geneva.
-
B.
distanceToZurich_km
Indicates the physical distance, measured in kilometers, between an entity’s location and the city of Zurich.
-
C.
distanceToLausanne
Indicates the measured distance between a given entity’s location and the city of Lausanne.
-
D.
distanceToBasel_km
Indicates the physical distance, measured in kilometers, between a given entity’s location and the city of Basel.
-
E.
distanceToBern_km
Indicates the distance, measured in kilometers, between an entity’s location and the city of Bern.
- 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_69d85a16c68c819099c1b547fbc87b32 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03ea4f13c819085d26fd32b5dca6f |
completed | April 16, 2026, 1:43 a.m. |
| PD | Predicate disambiguation | batch_69ded27b8cac8190bfa77698d53c5d1c |
completed | April 14, 2026, 11:49 p.m. |
Created at: April 10, 2026, 3:20 a.m.