Triple
T33603217
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Babahoyense |
E860776
|
entity |
| Predicate | languagePartOfSpeech |
P201816
|
FINISHED |
| Object | adjective |
—
|
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: adjective | Statement: [Babahoyense, languagePartOfSpeech, adjective]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languagePartOfSpeech Context triple: [Babahoyense, languagePartOfSpeech, adjective]
-
A.
partOfLanguage
Indicates that one linguistic element belongs to, is included within, or is a component of a particular language.
-
B.
languageTerm
Indicates that one entity is a linguistic expression (word, phrase, or term) used to denote or label the other entity.
-
C.
partOfLexicon
Indicates that a linguistic unit (such as a word or expression) belongs to or is included within a particular lexicon or vocabulary set.
-
D.
lexicalItem
Indicates that one entity is a word or vocabulary unit associated with, or used to express, another entity (such as a concept, meaning, or linguistic entry).
-
E.
etymologicalClass
Indicates the relationship between a term and the category or type of its origin or historical linguistic development.
- 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_69f3497f35908190a2e9bbb9b96c7a3f |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_6a00262d71488190a769783fb09e5803 |
completed | May 10, 2026, 6:31 a.m. |
| PD | Predicate disambiguation | batch_6a0023985f148190a335a3fb93e9981e |
completed | May 10, 2026, 6:20 a.m. |
| PDg | Predicate description generation | batch_6a00262cc9408190b95076a2d3da057d |
completed | May 10, 2026, 6:31 a.m. |
Created at: May 1, 2026, 1:41 a.m.