Triple
T27125271
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tenrikyo Church Headquarters |
E687108
|
entity |
| Predicate | hasOfficialNameInJapanese |
P9882
|
FINISHED |
| Object |
天理教教会本部
天理教教会本部は、日本発祥の新宗教である天理教の教義の中枢と全世界の教会組織を統括する本部機関である。
|
E1757603
|
NE 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: 天理教教会本部 | Statement: [Tenrikyo Church Headquarters, hasOfficialNameInJapanese, 天理教教会本部]
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: 天理教教会本部 Triple: [Tenrikyo Church Headquarters, hasOfficialNameInJapanese, 天理教教会本部]
Generated description
天理教教会本部は、日本発祥の新宗教である天理教の教義の中枢と全世界の教会組織を統括する本部機関である。
Provenance (5 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_69ef148c2b588190afc15b529f7af845 |
completed | April 27, 2026, 7:47 a.m. |
| NER | Named-entity recognition | batch_69f6244730408190b91939860d0cb25b |
completed | May 2, 2026, 4:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a12481925308190a0a239bf011ed315 |
completed | May 24, 2026, 12:36 a.m. |
| NEDg | Description generation | batch_6a1248bb58a48190ae84e7b538b10503 |
completed | May 24, 2026, 12:39 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a124973e3c88190898b0cece69419b3 |
completed | May 24, 2026, 12:42 a.m. |
Created at: April 27, 2026, 9:01 a.m.