Triple
T22622845
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Old Enchanted Balete Tree |
E558329
|
entity |
| Predicate | languageOfLocalFolklore |
P115774
|
FINISHED |
| Object | Cebuano |
—
|
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: Cebuano | Statement: [Old Enchanted Balete Tree, languageOfLocalFolklore, Cebuano]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageOfLocalFolklore Context triple: [Old Enchanted Balete Tree, languageOfLocalFolklore, Cebuano]
-
A.
languageOfSurroundingCulture
Indicates that one entity is the language predominantly used or characteristic of the surrounding culture associated with another entity.
-
B.
countryOfFolklore
Indicates the country with which a particular piece or tradition of folklore is associated or from which it originates.
-
C.
languageOfMyths
Indicates that the subject is the language in which the myths associated with the object are told or recorded.
-
D.
languageUsedInLocality
chosen
Indicates that a particular language is used or spoken within a specific locality or geographic area.
-
E.
vernacularOf
Indicates that one language or dialect is the everyday, locally used form corresponding to another, more general or standard 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_69e24545a8e08190bfa7482a2c725ff1 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f16e3ad6f48190b351a52e4b1d9b2d |
completed | April 29, 2026, 2:34 a.m. |
| PD | Predicate disambiguation | batch_69ee62855558819080da946c7b35a160 |
completed | April 26, 2026, 7:07 p.m. |
Created at: April 17, 2026, 3:01 p.m.