Triple
T11220738
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Magny-en-Vexin |
E265553
|
entity |
| Predicate | distanceFromParisKilometres |
P10703
|
FINISHED |
| Object | about 55 |
—
|
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 55 | Statement: [Magny-en-Vexin, distanceFromParisKilometres, about 55]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromParisKilometres Context triple: [Magny-en-Vexin, distanceFromParisKilometres, about 55]
-
A.
distanceFromParisCenter
chosen
Indicates the measured distance between a given location and the central point of Paris.
-
B.
distanceFromParisSaintLazare
Indicates the physical distance between a given place and Paris Saint-Lazare railway station.
-
C.
distanceFromFoixKilometres
Indicates the physical distance, measured in kilometers, between a given place or entity and the location of Foix.
-
D.
distanceToFrance
Indicates the spatial distance between a given entity and the country of France.
-
E.
distanceFromParisGareDeLyon
Indicates the distance between an entity and Paris Gare de Lyon railway station.
- 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_69d6aac59460819089b9848b27f57848 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e8eb84c48190b4f3bede254afde2 |
completed | April 9, 2026, 5:59 p.m. |
| PD | Predicate disambiguation | batch_69d75cfbbb188190861efd5d94fe27da |
completed | April 9, 2026, 8:02 a.m. |
Created at: April 8, 2026, 9:30 p.m.