Triple
T7715131
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 和光市 |
E174860
|
entity |
| Predicate | hasEnglishName |
P3437
|
FINISHED |
| Object | Wako |
E188130
|
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: Wako | Statement: [和光市, hasEnglishName, Wako]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wako Context triple: [和光市, hasEnglishName, Wako]
-
A.
Wako
chosen
Wako is a suburban city in Saitama Prefecture, Japan, located on the northern outskirts of Tokyo and known as a residential and commuter hub.
-
B.
Owada
Owada is a Japanese surname most notably borne by Empress Masako of Japan and her family.
-
C.
Nishiwaki
Nishiwaki is a city in central Hyōgo Prefecture, Japan, known for its location near the geographic center of the country and its mix of industrial and rural landscapes.
-
D.
Takaishi
Takaishi is a city in Osaka Prefecture, Japan, known as a small industrial and residential hub within the Osaka metropolitan area.
-
E.
Warekena
The Warekena are an Indigenous people of the Amazon region, primarily living along rivers in Brazil and Venezuela, known for their distinct Arawakan language and traditional riverine lifestyle.
- 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_69c6995c463c8190a14458036249d419 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c702cbe74081908502ac670515fa3c |
completed | March 27, 2026, 10:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c99ba1bbdc81909269ac0a97caa91d |
completed | March 29, 2026, 9:37 p.m. |
Created at: March 27, 2026, 4:04 p.m.