Triple
T14633171
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Châteaudun |
E343531
|
entity |
| Predicate | distanceFromChartres |
P115128
|
FINISHED |
| Object | approximately 45 km southwest |
—
|
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 45 km southwest | Statement: [Châteaudun, distanceFromChartres, approximately 45 km southwest]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromChartres Context triple: [Châteaudun, distanceFromChartres, approximately 45 km southwest]
-
A.
distanceFromAvignon
Indicates the spatial distance separating a given entity or location from Avignon.
-
B.
distanceFromStrasbourg
Indicates the spatial distance between a given place or entity and the city of Strasbourg.
-
C.
distanceToRouen
Indicates the spatial distance between a given entity and the location of Rouen.
-
D.
distanceFromLyon
Indicates the spatial distance between a given entity and the city of Lyon.
-
E.
distanceFromFoixKilometres
Indicates the physical distance, measured in kilometers, between a given place or entity and the location of Foix.
- 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_69d822dffc3c8190aa173b90761bffda |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb4aa7cb48190b008bd6b0e162c89 |
completed | April 14, 2026, 9:42 p.m. |
| PD | Predicate disambiguation | batch_69de657359c88190b082e3e9f86fc1d7 |
completed | April 14, 2026, 4:04 p.m. |
| PDg | Predicate description generation | batch_69de716c17cc8190aeb85296abee85a7 |
completed | April 14, 2026, 4:55 p.m. |
Created at: April 10, 2026, 1:26 a.m.