Triple
T34610971
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vendôme |
E888734
|
entity |
| Predicate | distanceToBlois_km |
P205485
|
FINISHED |
| Object | about 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: about 35 | Statement: [Vendôme, distanceToBlois_km, about 35]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToBlois_km Context triple: [Vendôme, distanceToBlois_km, about 35]
-
A.
distanceFromFoixKilometres
Indicates the physical distance, measured in kilometers, between a given place or entity and the location of Foix.
-
B.
distanceToDijon_km
Indicates the physical distance, measured in kilometers, between a given entity’s location and the city of Dijon.
-
C.
distanceFromOrléans
Indicates the spatial distance between a given location and the city of Orléans.
-
D.
distanceFromAngersKilometres
Indicates the physical distance, measured in kilometers, between an entity and the location of Angers.
-
E.
distanceToBordeauxCenter
Indicates the measured or calculated distance between a given entity’s location and the center of Bordeaux.
- 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_69f349d584e08190b40b9f6281ad50c4 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_6a037c92f03c8190ae2751270b195423 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379fd7aac8190873077e63873aa72 |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c80ba448190853011097a151b7e |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 1, 2026, 2:03 a.m.