Triple
T12780123
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chūō, Tokyo |
E305484
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Ningyōchō |
E569188
|
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: Ningyōchō | Statement: [Chūō, Tokyo, contains, Ningyōchō]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ningyōchō Context triple: [Chūō, Tokyo, contains, Ningyōchō]
-
A.
Ningyōchō
chosen
Ningyōchō is a traditional downtown neighborhood in Tokyo known for its old-style shops, restaurants, and remnants of the city’s Edo-period atmosphere.
-
B.
Bakurochō
Bakurochō is a commercial district in Tokyo known historically as a wholesale textile and clothing center with convenient access to major rail and transit lines.
-
C.
Shōji-ko
Shōji-ko is one of the Fuji Five Lakes in Yamanashi Prefecture, Japan, known for its scenic views of Mount Fuji and relatively undeveloped, tranquil surroundings.
-
D.
Kizoku-in
Kizoku-in was the upper house of Japan’s prewar Imperial Diet, composed mainly of nobility and imperial appointees.
-
E.
Ameya-Yokochō
Ameya-Yokochō is a bustling open-air market street in Tokyo known for its dense concentration of shops, food stalls, and bargain goods.
- 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_69d7bdf2b43c819098ae5aa68e61ea58 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96e5a5680819095dcd491486d23e7 |
completed | April 10, 2026, 9:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6a5427be88190956c616b832d9841 |
completed | May 3, 2026, 1:30 a.m. |
Created at: April 9, 2026, 5:29 p.m.