Triple
T3042680
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Château Trotanoy |
E83166
|
entity |
| Predicate | countrySubregion |
P44769
|
FINISHED |
| Object | southwest France |
—
|
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: southwest France | Statement: [Château Trotanoy, countrySubregion, southwest France]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: countrySubregion Context triple: [Château Trotanoy, countrySubregion, southwest France]
-
A.
subregionOf
Indicates that one region is geographically or administratively contained within, and is a part of, another larger region.
-
B.
countrySubdivision
Indicates that one geopolitical region is an administrative or territorial subdivision of a larger country.
-
C.
countrySubdivisionType
Indicates the specific type or category of an administrative or territorial subdivision within a country (e.g., state, province, region).
-
D.
countryRegion
Indicates that a country is located within, or belongs to, a specific geographic or administrative region.
-
E.
subregionCodeFor
Indicates that one entity is the code assigned to identify a specific subregion of the other entity.
- 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_69ad8b2298908190a7cb4e9bdbf064d0 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9b5d2a308190b4ce20efcae9b761 |
completed | March 8, 2026, 3:53 p.m. |
| PD | Predicate disambiguation | batch_69ad961fc62c819087c4c3a44b00847d |
completed | March 8, 2026, 3:30 p.m. |
| PDg | Predicate description generation | batch_69ad97f6af3881909f4547967384114c |
completed | March 8, 2026, 3:38 p.m. |
Created at: March 8, 2026, 3:01 p.m.