Triple
T14324843
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ginza Wako building |
E355188
|
entity |
| Predicate | brand |
P1500
|
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: [Ginza Wako building, brand, Wako]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wako Context triple: [Ginza Wako building, brand, 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.
Wazanaki
Wazanaki are the Zanaki people of Tanzania, a Bantu-speaking ethnic group traditionally living near Lake Victoria.
-
D.
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.
-
E.
Takaishi
Takaishi is a city in Osaka Prefecture, Japan, known as a small industrial and residential hub within the Osaka metropolitan area.
- 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_69d8278fa2108190bc0d0e7939c1eb03 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de883e6a288190b6c22f630a1eef3c |
completed | April 14, 2026, 6:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fedd1bb1c48190b5d2b4167c756abf |
completed | May 9, 2026, 7:07 a.m. |
Created at: April 10, 2026, 1:13 a.m.