Triple
T11055933
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rayados |
E261375
|
entity |
| Predicate | associatedCityDemonym |
P191
|
FINISHED |
| Object | Regio |
—
|
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: Regio | Statement: [Rayados, associatedCityDemonym, Regio]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedCityDemonym Context triple: [Rayados, associatedCityDemonym, Regio]
-
A.
relatedDemonym
Indicates that one entity is the demonym (name for residents or natives) associated with the other entity.
-
B.
hasDemonym
chosen
Indicates that one entity is the term (demonym) used to refer to the inhabitants or natives of another entity (typically a place).
-
C.
populationDemonym
Indicates the term used to refer to the people or inhabitants associated with a particular place or region.
-
D.
hasDemonymLanguage
Indicates that a language is used as the demonym (people’s name or adjective of nationality) for inhabitants of a particular place or group.
-
E.
modernCityNameLanguage
Indicates that the modern name of a city is expressed in a particular language.
- 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_69d6aa98650481908609c7c56bfa7902 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d798a152b4819095b74a8996346077 |
completed | April 9, 2026, 12:16 p.m. |
| PD | Predicate disambiguation | batch_69d7440da46c8190a77380d5d747ac9c |
completed | April 9, 2026, 6:15 a.m. |
Created at: April 8, 2026, 9:26 p.m.