Triple
T27427419
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Xcalakoop San Juan Bautista |
E690527
|
entity |
| Predicate | likelyLocalLanguage |
P115774
|
FINISHED |
| Object | Yucatec Maya |
—
|
NE NERFINISHED |
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: Yucatec Maya | Statement: [Xcalakoop San Juan Bautista, likelyLocalLanguage, Yucatec Maya]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: likelyLocalLanguage Context triple: [Xcalakoop San Juan Bautista, likelyLocalLanguage, Yucatec Maya]
-
A.
possibleLanguage
Indicates that an entity could plausibly be expressed, interpreted, or communicated in a given language.
-
B.
languageUsedInLocality
chosen
Indicates that a particular language is used or spoken within a specific locality or geographic area.
-
C.
recognizedRegionalLanguage
Indicates that a language holds officially recognized status within a specific region or subnational jurisdiction.
-
D.
hasPrimaryLanguageNearby
Indicates that an entity is associated with a primary language that is predominantly used or present in its immediate geographic or contextual vicinity.
-
E.
languageOfSurroundingCountry
Indicates that a language is the primary or commonly used language in the country surrounding a given place or region.
- 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_69ef52003fb48190b0f1295246182a86 |
completed | April 27, 2026, 12:09 p.m. |
| NER | Named-entity recognition | batch_69f661b58ac48190907b6c6e9ccc2c59 |
completed | May 2, 2026, 8:42 p.m. |
| PD | Predicate disambiguation | batch_69f660eea4648190b0d5e24293607813 |
completed | May 2, 2026, 8:39 p.m. |
Created at: April 27, 2026, 12:41 p.m.