Triple
T2976397
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mazatec |
E80407
|
entity |
| Predicate | hasISO639Code |
P8719
|
FINISHED |
| Object | maz |
—
|
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: maz | Statement: [Mazatec, hasISO639Code, maz]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasISO639Code Context triple: [Mazatec, hasISO639Code, maz]
-
A.
hasISO6393Code
chosen
Indicates that a language or linguistic entity is associated with a specific ISO 639-3 three-letter language code.
-
B.
hasISO639_5Code
Indicates that a language or language group is associated with a specific ISO 639-5 code that identifies it within the ISO 639-5 language classification standard.
-
C.
hasISOCode
Indicates that an entity is associated with a specific standardized ISO code that uniquely identifies it according to ISO conventions.
-
D.
languageCodeISO639-1
Indicates that the subject entity is associated with the specified two-letter ISO 639-1 language code.
-
E.
languageCodeISO639-2
Indicates that an entity is associated with a language identified by its ISO 639-2 three-letter code.
- 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_69ad8b15f6ac8190be5fd16a33edcb4f |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad998ad5308190a012ec4940eb46cb |
completed | March 8, 2026, 3:45 p.m. |
| PD | Predicate disambiguation | batch_69ad96105a708190a9ec4838cbcb1207 |
completed | March 8, 2026, 3:30 p.m. |
Created at: March 8, 2026, 2:58 p.m.