Triple
T1527457
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tglg |
E32366
|
entity |
| Predicate | associatedLanguages |
P16462
|
FINISHED |
| Object | Philippine languages |
—
|
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: Philippine languages | Statement: [Tglg, associatedLanguages, Philippine languages]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedLanguages Context triple: [Tglg, associatedLanguages, Philippine languages]
-
A.
influencedLanguage
Indicates that one language has had an effect on the development, structure, or usage of another language.
-
B.
associatedLanguageScript
chosen
Indicates that there is a relationship between a language and the script or writing system used to represent it.
-
C.
usesWorkingLanguagesOf
Indicates that one entity employs or operates using the working languages associated with another entity.
-
D.
hasNeighboringLanguages
Indicates that two languages are geographically or regionally adjacent to each other in their areas of use.
-
E.
isWorkingLanguageOf
Indicates that a particular language is officially used as a medium of work, communication, or operation within a specified organization, institution, or context.
- 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_69a885e9b0ac819093a9806ad0efc82c |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a933ddc5a881909cdf503f2bc29bd4 |
completed | March 5, 2026, 7:42 a.m. |
| PD | Predicate disambiguation | batch_69a907ae8f688190ad9000ea1e018585 |
completed | March 5, 2026, 4:33 a.m. |
Created at: March 4, 2026, 7:26 p.m.