Triple
T36943304
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cebu–Davao |
E913833
|
entity |
| Predicate | languageUsedInOperations |
P58450
|
FINISHED |
| Object | English |
—
|
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: English | Statement: [Cebu–Davao, languageUsedInOperations, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageUsedInOperations Context triple: [Cebu–Davao, languageUsedInOperations, English]
-
A.
languageOfOperation
Indicates the language in which an entity (such as a system, service, or process) primarily operates or functions.
-
B.
languageOfOperator
Indicates that a particular language is used by, or associated with, a given operator in performing its functions or services.
-
C.
languagesUsed
chosen
Indicates that one entity uses, employs, or is expressed in one or more languages associated with the other entity.
-
D.
tertiaryLanguageOfOperation
Indicates that an entity uses a specified language as its third most prominent or prioritized language of operation.
-
E.
hasPrimaryLanguageOfOperations
Indicates that an entity conducts its main activities or operations primarily using a specified language.
- 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_69f76e8a6a5c81909c1febf32bf3fe23 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_6a015ff02814819094806517fc4c69fa |
completed | May 11, 2026, 4:49 a.m. |
| PD | Predicate disambiguation | batch_6a0154ddd3c48190b85f9f48731cfd8f |
completed | May 11, 2026, 4:02 a.m. |
Created at: May 3, 2026, 4:13 p.m.