Triple
T2054342
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Binisaya |
E45639
|
entity |
| Predicate | lexifierFor |
P11298
|
FINISHED |
| Object | Chavacano Cebuano-based creoles |
—
|
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: Chavacano Cebuano-based creoles | Statement: [Binisaya, lexifierFor, Chavacano Cebuano-based creoles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lexifierFor Context triple: [Binisaya, lexifierFor, Chavacano Cebuano-based creoles]
-
A.
lexifierLanguage
chosen
Indicates that one language serves as the primary source or base language from which the core vocabulary and structure of another language, typically a pidgin or creole, are derived.
-
B.
linguisticRegister
Indicates the level of formality or stylistic variety in which a linguistic expression is typically used within a given context.
-
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.
languageDesigned
Indicates that one entity created or developed the language used or associated with another entity.
-
E.
linguisticFeature
Indicates a relationship where a linguistic property, pattern, or characteristic is attributed to or associated with a language-related entity (such as a word, phrase, or text).
- 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_69a8891a19508190a12ef1e192308dcb |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abb9a8518081909ba95a8ef9321f12 |
completed | March 7, 2026, 5:37 a.m. |
| PD | Predicate disambiguation | batch_69abb7abba508190b872f345d3ba51bb |
completed | March 7, 2026, 5:29 a.m. |
Created at: March 4, 2026, 7:39 p.m.