Triple
T21350024
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hachiman shrine |
E526449
|
entity |
| Predicate | JapaneseNameReading |
P143799
|
FINISHED |
| Object | Hachimangū |
—
|
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: Hachimangū | Statement: [Hachiman shrine, JapaneseNameReading, Hachimangū]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: JapaneseNameReading Context triple: [Hachiman shrine, JapaneseNameReading, Hachimangū]
-
A.
onYomiJapanese
Indicates that the specified reading is the on’yomi (Sino-Japanese) pronunciation associated with a given kanji or term.
-
B.
japaneseKunReading
Indicates that a Japanese kanji character has a specific native Japanese (kun) reading associated with it.
-
C.
hasKanjiReading
Indicates that a written kanji character is associated with a specific reading or pronunciation.
-
D.
japaneseOnReading
Indicates the on-yomi (Sino-Japanese) pronunciation associated with a given Japanese kanji or term.
-
E.
JapaneseNameOrigin
Indicates that one entity’s name originates from or is derived from the Japanese language or naming tradition in relation to another entity.
- F. None of above. chosen
Provenance (4 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_69e0b51cd5cc81909ac1187971e8a8ad |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e8ad30512081909012ce318fa67679 |
completed | April 22, 2026, 11:12 a.m. |
| PD | Predicate disambiguation | batch_69e6161feea4819091d13bb003363279 |
completed | April 20, 2026, 12:03 p.m. |
| PDg | Predicate description generation | batch_69e6190163448190a2404b396215c686 |
completed | April 20, 2026, 12:16 p.m. |
Created at: April 16, 2026, 5:03 p.m.