Triple
T12220828
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Loiceño |
E291209
|
entity |
| Predicate | partOfLinguisticCategory |
P42778
|
FINISHED |
| Object | gentilicio (Spanish demonym) |
—
|
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: gentilicio (Spanish demonym) | Statement: [Loiceño, partOfLinguisticCategory, gentilicio (Spanish demonym)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: partOfLinguisticCategory Context triple: [Loiceño, partOfLinguisticCategory, gentilicio (Spanish demonym)]
-
A.
partOfLanguage
Indicates that one linguistic element belongs to, is included within, or is a component of a particular language.
-
B.
partOfLexicon
chosen
Indicates that a linguistic unit (such as a word or expression) belongs to or is included within a particular lexicon or vocabulary set.
-
C.
linguisticType
Indicates the type or category of language or linguistic system associated with an entity (e.g., spoken, signed, written, or other linguistic modality).
-
D.
morphologicalClass
Indicates the classification of an entity based on its morphological form or structural pattern.
-
E.
linguisticClassification
Indicates the relationship by which an entity is categorized according to its language or linguistic type.
- 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_69d6ab668acc8190963ba424049d6aee |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d920e312708190b4aede2e21f5f697 |
completed | April 10, 2026, 4:10 p.m. |
| PD | Predicate disambiguation | batch_69d91c3d669c81908eea7ad61122d275 |
completed | April 10, 2026, 3:50 p.m. |
Created at: April 8, 2026, 9:51 p.m.