Triple
T8411993
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sorgues |
E198645
|
entity |
| Predicate | distanceFromAvignon |
P81854
|
FINISHED |
| Object | approximately 10 km north |
—
|
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 10 km north | Statement: [Sorgues, distanceFromAvignon, approximately 10 km north]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromAvignon Context triple: [Sorgues, distanceFromAvignon, approximately 10 km north]
-
A.
distanceFromStrasbourg
Indicates the spatial distance between a given place or entity and the city of Strasbourg.
-
B.
distanceFromLyon
Indicates the spatial distance between a given entity and the city of Lyon.
-
C.
distanceToLePuy-en-Velay
Indicates the spatial distance between a given entity and the location of Le Puy-en-Velay.
-
D.
distanceToPontDuGard
Indicates the measured spatial distance between a given entity and the Pont du Gard.
-
E.
distanceToMarseilleKilometers
Indicates the physical distance, measured in kilometers, between a given location or entity and the city of Marseille.
- 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_69ca831201b481909e137936ef99ff11 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cb83e0341c819080506e696131671e |
completed | March 31, 2026, 8:20 a.m. |
| PD | Predicate disambiguation | batch_69cb70d473dc8190af8ea81ee5aa970d |
completed | March 31, 2026, 6:59 a.m. |
| PDg | Predicate description generation | batch_69cb76da264881909483b835e1db06da |
completed | March 31, 2026, 7:25 a.m. |
Created at: March 30, 2026, 6:05 p.m.