Triple
T4214736
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tsukuba |
E94187
|
entity |
| Predicate | hasJapaneseWritingSystem |
P28734
|
FINISHED |
| Object | Kanji: つくば市 |
—
|
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: Kanji: つくば市 | Statement: [Tsukuba, hasJapaneseWritingSystem, Kanji: つくば市]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasJapaneseWritingSystem Context triple: [Tsukuba, hasJapaneseWritingSystem, Kanji: つくば市]
-
A.
hasJapaneseText
Indicates that an entity contains or is associated with text written in the Japanese language.
-
B.
usesKatakanaFor
Indicates that one entity is written or represented using katakana script in relation to another entity.
-
C.
hasHakkaRomanization
Indicates that an entity is associated with a specific representation of its name or term in Hakka Romanization.
-
D.
hasWritingSystemForMajorLanguage
Indicates that there exists a writing system used to represent a major language associated with the given entity.
-
E.
hasNameInJapanese
chosen
Indicates that an entity is associated with a specific name expressed in the Japanese language.
- 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_69b3451997e08190851db4a9a588837d |
completed | March 12, 2026, 10:58 p.m. |
| NER | Named-entity recognition | batch_69b34e098da881909a0cc339cc186627 |
completed | March 12, 2026, 11:36 p.m. |
| PD | Predicate disambiguation | batch_69b347efd9b08190bb50f82e4e7fe06d |
completed | March 12, 2026, 11:10 p.m. |
Created at: March 12, 2026, 11:04 p.m.