Triple
T3761644
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Limousin (de facto shared functions) |
E82173
|
entity |
| Predicate | geographicalCharacter |
P12436
|
FINISHED |
| Object | predominantly rural |
—
|
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: predominantly rural | Statement: [Limousin (de facto shared functions), geographicalCharacter, predominantly rural]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: geographicalCharacter Context triple: [Limousin (de facto shared functions), geographicalCharacter, predominantly rural]
-
A.
regionCharacter
Indicates a characteristic, feature, or quality that typifies or defines a particular region.
-
B.
hasGeographyCharacteristic
chosen
Indicates that an entity possesses a specific geographical feature, property, or attribute.
-
C.
landerName
Indicates the specific name assigned to a lander involved in a mission or operation.
-
D.
geographicContext
Indicates that one entity is situated within, associated with, or characterized by the geographic setting or region defined by another entity.
-
E.
notableCharacterType
Indicates that an entity is a notable or prominent example of a specified character type or role.
- 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_69ad8b1db40081908b61ffa6b78afd4d |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adcbc553a08190ba361675901496ed |
completed | March 8, 2026, 7:19 p.m. |
| PD | Predicate disambiguation | batch_69adc04c851c8190ae5eaebf36df539b |
completed | March 8, 2026, 6:30 p.m. |
Created at: March 8, 2026, 3:35 p.m.