Triple
T23217903
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sakizaya |
E580799
|
entity |
| Predicate | hasAutonymLanguageName |
P70102
|
FINISHED |
| Object | Sakizaya |
—
|
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: Sakizaya | Statement: [Sakizaya, hasAutonymLanguageName, Sakizaya]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAutonymLanguageName Context triple: [Sakizaya, hasAutonymLanguageName, Sakizaya]
-
A.
hasEndonymLanguage
chosen
Indicates that the language specified is the one in which a name or term is expressed in its own native or local form.
-
B.
hasNameInLocalLanguage
Indicates that an entity is associated with a name expressed in the local or native language of a given context or region.
-
C.
hasEndonym
Indicates that an entity has a name or designation used by native speakers or within its own local language or community.
-
D.
autonymLanguageCode
Indicates that the associated language code is the one used by a language to refer to itself (its autonym).
-
E.
hasFullNameLanguage
Indicates that the language in which a full name is expressed is associated with that full name.
- 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_69e2460389408190be74f41d217799a9 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f1916653f08190a7dcbc659c6b6a25 |
completed | April 29, 2026, 5:04 a.m. |
| PD | Predicate disambiguation | batch_69effcccee508190a7ae311fdd319806 |
completed | April 28, 2026, 12:18 a.m. |
Created at: April 17, 2026, 4:08 p.m.