Triple
T19447285
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Remiremont |
E486511
|
entity |
| Predicate | distanceToStrasbourgKilometres |
P42703
|
FINISHED |
| Object | about 140 |
—
|
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 140 | Statement: [Remiremont, distanceToStrasbourgKilometres, about 140]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToStrasbourgKilometres Context triple: [Remiremont, distanceToStrasbourgKilometres, about 140]
-
A.
distanceFromStrasbourg
chosen
Indicates the spatial distance between a given place or entity and the city of Strasbourg.
-
B.
distanceFromBesançonKilometres
Indicates the distance, measured in kilometers, between an entity and the city of Besançon.
-
C.
distanceToMetzKilometers
Indicates the physical distance, measured in kilometers, between a given entity’s location and the city of Metz.
-
D.
distanceToSaarbrücken
Indicates the spatial distance between a given entity and the location of Saarbrücken.
-
E.
distanceFromNiceByRoad_km
Indicates the length of the road route, in kilometers, from the city of Nice to the given location.
- 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_69d8e8d7ad488190a3373045029b0f3b |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e6338b25d88190bc137a411576c73f |
completed | April 20, 2026, 2:09 p.m. |
| PD | Predicate disambiguation | batch_69e4fd6e806081909053f325ba01ab6b |
completed | April 19, 2026, 4:06 p.m. |
Created at: April 10, 2026, 1:38 p.m.