Triple
T27326998
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Xcalakoop San Marcos |
E689678
|
entity |
| Predicate | likelyLanguageUsed |
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 Marcos, likelyLanguageUsed, Yucatec Maya]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: likelyLanguageUsed Context triple: [Xcalakoop San Marcos, likelyLanguageUsed, Yucatec Maya]
-
A.
languageUsedAs
Indicates that one language is employed in a specific role, function, or context relative to another entity or situation.
-
B.
languagesUsed
Indicates that one entity uses, employs, or is expressed in one or more languages associated with the other entity.
-
C.
probableLanguageType
Indicates that something is likely to belong to, be expressed in, or be categorized under a particular language or type of language.
-
D.
languageAssociation
Indicates an association or relationship between entities based on a language they use, represent, or are linked to.
-
E.
languageUsedInLocality
chosen
Indicates that a particular language is used or spoken within a specific locality or geographic area.
- 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_69ef355d4cb08190ab032c0a2e7d3753 |
completed | April 27, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69f6562fd3488190be1acd8c526a28d2 |
completed | May 2, 2026, 7:53 p.m. |
| PD | Predicate disambiguation | batch_69f651a931748190a637e631a52bbfaa |
completed | May 2, 2026, 7:34 p.m. |
Created at: April 27, 2026, 11:36 a.m.