Triple
T14437519
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Taitō ward |
E358002
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Nezu |
E322452
|
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: Nezu | Statement: [Taitō ward, contains, Nezu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nezu Context triple: [Taitō ward, contains, Nezu]
-
A.
Nezu
chosen
Nezu is a traditional neighborhood in Tokyo known for its historic Nezu Shrine, old-town atmosphere, and preserved shitamachi streets.
-
B.
Takayoshi
Takayoshi is a Japanese given name notably borne by Kido Takayoshi, a key samurai and statesman of the Meiji Restoration.
-
C.
Wakatoshi
Wakatoshi is a fictional volleyball player from the manga and anime series "Haikyuu!!", known as the powerful ace and captain of Shiratorizawa Academy.
-
D.
Kazuno
Kazuno is a city in northern Japan known for its hot springs, traditional festivals, and mountainous rural scenery.
-
E.
Yasu
Yasu is a Japanese city located in Shiga Prefecture, known for its blend of residential areas, local industry, and proximity to Lake Biwa.
- 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_69d8279402a88190821ffa39ae15bccf |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de914a45ec81909ab8ccf302047d7f |
completed | April 14, 2026, 7:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff6ec12de8819097cd83530e54f54b |
completed | May 9, 2026, 5:28 p.m. |
Created at: April 10, 2026, 1:18 a.m.