Triple
T8052603
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Reona Esaki |
E187710
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Esaki |
E314661
|
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: Esaki | Statement: [Reona Esaki, familyName, Esaki]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Esaki Context triple: [Reona Esaki, familyName, Esaki]
-
A.
Esaki
chosen
Esaki is a Japanese surname most notably associated with physicist Leo Esaki, a Nobel laureate recognized for his pioneering work on quantum tunneling in semiconductors.
-
B.
Kamiyama
Kamiyama is a Japanese surname borne by various individuals, including artists, athletes, and public figures.
-
C.
Munakata
Munakata is a coastal city in Japan known for its ancient Munakata Taisha Shinto shrines and its location in northern Fukuoka Prefecture on Kyushu Island.
-
D.
Sakae
Sakae is a major downtown commercial and entertainment district in Nagoya, Japan, known for its shopping, nightlife, and landmark attractions.
-
E.
Yukuhashi
Yukuhashi is a city in eastern Fukuoka Prefecture, Japan, known as a regional commercial and transportation hub on Kyushu.
- 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_69ca82b15e948190a62fd7af5218426a |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3f7c425c8190aa1b2f534afeb58c |
completed | March 31, 2026, 3:29 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cef298986c8190a253d5c61310a23a |
completed | April 2, 2026, 10:50 p.m. |
Created at: March 30, 2026, 5:25 p.m.