Triple
T27519859
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vésulienne |
E694675
|
entity |
| Predicate | countryOfRelatedPlace |
P118930
|
FINISHED |
| Object | France |
—
|
NE NERFINISHED |
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: France | Statement: [Vésulienne, countryOfRelatedPlace, France]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: countryOfRelatedPlace Context triple: [Vésulienne, countryOfRelatedPlace, France]
-
A.
countryOfReferent
Indicates that one entity is the country with which the referenced entity (the referent) is associated or to which it belongs.
-
B.
countryOf
chosen
Indicates that one entity is the country to which another entity belongs, is located in, or is associated with.
-
C.
relatedCountry
Indicates that there is a relevant or associated relationship between an entity and a specified country, without specifying the exact nature of that relationship.
-
D.
countryOfEponym
Indicates that the related entity is named after something (an eponym) originating from or associated with a particular country.
-
E.
countryOfCityReferredTo
Indicates that one entity is the country in which the referenced city entity is located.
- 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_69ef538550208190aa9de8e2cb260d93 |
completed | April 27, 2026, 12:16 p.m. |
| NER | Named-entity recognition | batch_69fd553d7cb881908d243e7a9f30ac85 |
completed | May 8, 2026, 3:15 a.m. |
| PD | Predicate disambiguation | batch_69fd514dcb1c81908333c70d7edd79c9 |
completed | May 8, 2026, 2:58 a.m. |
Created at: April 27, 2026, 1:20 p.m.