Triple
T13146100
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kengo Kora |
E312337
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Kengo |
E312337
|
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: Kengo | Statement: [Kengo Kora, givenName, Kengo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kengo Context triple: [Kengo Kora, givenName, Kengo]
-
A.
Kengo Kora
chosen
Kengo Kora is a Japanese actor known for his roles in contemporary Japanese cinema and television dramas.
-
B.
Kentarō
Kentarō is a Japanese given name commonly used for males, often associated with traditional or strong-sounding name combinations.
-
C.
Kazuno
Kazuno is a city in northern Japan known for its hot springs, traditional festivals, and mountainous rural scenery.
-
D.
Kouya
Kouya is a language of the Central Tano branch of the Niger-Congo family, spoken by a community in West Africa.
-
E.
Katsuya
Katsuya is a Japanese given name commonly used for males.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d806aabde48190899e13e41659cae5 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98bcf6d0c819081d078f33e4bdedc |
completed | April 10, 2026, 11:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f72660f8d48190b437a57f2a75f6ae |
completed | May 3, 2026, 10:41 a.m. |
Created at: April 9, 2026, 9:10 p.m.