Triple
T38580714
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hatsu-uma |
E932235
|
entity |
| Predicate | hasLocalVariations |
P32680
|
FINISHED |
| Object | regional customs and rituals |
—
|
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: regional customs and rituals | Statement: [Hatsu-uma, hasLocalVariations, regional customs and rituals]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLocalVariations Context triple: [Hatsu-uma, hasLocalVariations, regional customs and rituals]
-
A.
hasRegionalVariationsIn
chosen
Indicates that something exhibits different forms, versions, or characteristics depending on the geographic region.
-
B.
usesLocalLanguageVariant
Indicates that an entity employs a region-specific or localized form of a language rather than a standard or global variant.
-
C.
hasLinguisticVariety
Indicates that one entity possesses or exhibits a particular linguistic variety in relation to another entity or context.
-
D.
hasColloquialVariety
Indicates that one linguistic form, expression, or variety is an informal, colloquial counterpart or version of another.
-
E.
linguisticVariant
Indicates that one linguistic form is an alternative version or expression of another within the same or closely related language context.
- 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_69f76ec654d48190b421111cf26e54d9 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69ffde9263248190996f970b6cf6e49d |
completed | May 10, 2026, 1:25 a.m. |
| PD | Predicate disambiguation | batch_69ffdd760f1c8190abc6c0c1cd97ba5f |
completed | May 10, 2026, 1:20 a.m. |
Created at: May 3, 2026, 4:32 p.m.