Triple
T8176798
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fuentealbilla |
E190955
|
entity |
| Predicate | hasDemonymLanguage |
P81270
|
FINISHED |
| Object | Spanish |
—
|
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: Spanish | Statement: [Fuentealbilla, hasDemonymLanguage, Spanish]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDemonymLanguage Context triple: [Fuentealbilla, hasDemonymLanguage, Spanish]
-
A.
hasDemonym
Indicates that one entity is the term (demonym) used to refer to the inhabitants or natives of another entity (typically a place).
-
B.
hasLanguageOfToponym
Indicates that a place name (toponym) is expressed in or associated with a particular language.
-
C.
hasExonym
Indicates that one entity is known by an alternative name or designation in another language or cultural context.
-
D.
hasEthnonymCountry
Indicates that an ethnonym (a name for an ethnic group or people) is associated with or pertains to a specific country.
-
E.
relatedDemonym
Indicates that one entity is the demonym (name for residents or natives) associated with 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_69ca82c1c0a08190bf8692b4d91a03ca |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb4aba0dd88190828080d0d89612eb |
completed | March 31, 2026, 4:16 a.m. |
| PD | Predicate disambiguation | batch_69cb36a7952481908f34e3e82f375a84 |
completed | March 31, 2026, 2:51 a.m. |
| PDg | Predicate description generation | batch_69cb45503eec8190aeef0da6c3324710 |
completed | March 31, 2026, 3:53 a.m. |
Created at: March 30, 2026, 5:40 p.m.