Triple
T15620585
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Soka Gakkai Women’s Division |
E375539
|
entity |
| Predicate | hasLanguageOfActivity |
P9278
|
FINISHED |
| Object | Japanese |
—
|
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: Japanese | Statement: [Soka Gakkai Women’s Division, hasLanguageOfActivity, Japanese]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLanguageOfActivity Context triple: [Soka Gakkai Women’s Division, hasLanguageOfActivity, Japanese]
-
A.
hasLanguageOn
chosen
Indicates that an entity uses or is associated with a particular language in a specific context, medium, or location.
-
B.
usesWorkingLanguagesOf
Indicates that one entity employs or operates using the working languages associated with another entity.
-
C.
hasLanguageStatus
Indicates that an entity has a particular status or condition regarding its language use, recognition, or classification.
-
D.
isLanguageOf
Indicates that a particular language is used as the official or primary language associated with a given entity (such as a person, document, or region).
-
E.
languageOfExpression
Indicates that a particular language is used as the medium or form in which an expression (such as a text, utterance, or work) is realized.
- 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_69d85ccf2794819096cda4cbcb02d478 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04e9a95f08190b0013ba1428849d3 |
completed | April 16, 2026, 2:51 a.m. |
| PD | Predicate disambiguation | batch_69deda868d4481908f4bce1c64d2902a |
completed | April 15, 2026, 12:23 a.m. |
Created at: April 10, 2026, 4:13 a.m.