Triple
T14594887
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Durtal |
E342531
|
entity |
| Predicate | distanceFromAngersKilometres |
P114977
|
FINISHED |
| Object | approximately 35 |
—
|
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 35 | Statement: [Durtal, distanceFromAngersKilometres, approximately 35]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromAngersKilometres Context triple: [Durtal, distanceFromAngersKilometres, approximately 35]
-
A.
distanceFromFoixKilometres
Indicates the physical distance, measured in kilometers, between a given place or entity and the location of Foix.
-
B.
distanceToSaint-Étienne
Indicates the measured or specified distance between a given entity and the location Saint-Étienne.
-
C.
distanceFromLyon
Indicates the spatial distance between a given entity and the city of Lyon.
-
D.
distanceFromBesançonKilometres
Indicates the distance, measured in kilometers, between an entity and the city of Besançon.
-
E.
distanceToClermontFerrand_km
Indicates the physical distance, measured in kilometers, between a given place and Clermont-Ferrand.
- 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_69d822ddc0f081909cd8163c7de298cd |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb43581348190b5362251c3a89654 |
completed | April 14, 2026, 9:40 p.m. |
| PD | Predicate disambiguation | batch_69de656a953481909a4645b004c40de7 |
completed | April 14, 2026, 4:03 p.m. |
| PDg | Predicate description generation | batch_69de716c17cc8190aeb85296abee85a7 |
completed | April 14, 2026, 4:55 p.m. |
Created at: April 10, 2026, 1:24 a.m.