Triple
T23720361
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 一子 |
E586124
|
entity |
| Predicate | hasVariantReading |
P59286
|
FINISHED |
| Object | multiple possible readings in 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: multiple possible readings in Japanese | Statement: [一子, hasVariantReading, multiple possible readings in Japanese]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasVariantReading Context triple: [一子, hasVariantReading, multiple possible readings in Japanese]
-
A.
hasVariantReadingsWith
chosen
Indicates a relationship where two textual items are linked because they exhibit differing or alternative readings of (typically) the same underlying content.
-
B.
hasVariantSpelling
Indicates that one term is an alternative spelling form of another term.
-
C.
hasVariant
Indicates that one entity exists as an alternative form, version, or variation of another entity.
-
D.
hasVietnameseReading
Indicates that an entity is associated with a specific reading or pronunciation in the Vietnamese language.
-
E.
hasVariantText
Indicates that an entity is associated with an alternative or differing textual form of its content.
- 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_69e24906fb108190a6898751e46bdc11 |
completed | April 17, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69f1b910759c8190be189db3e86d7258 |
completed | April 29, 2026, 7:53 a.m. |
| PD | Predicate disambiguation | batch_69f155e4b1148190836ede4741dcb888 |
completed | April 29, 2026, 12:50 a.m. |
Created at: April 17, 2026, 7 p.m.