Triple
T15399702
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daruma Market |
E368277
|
entity |
| Predicate | hasLocalName |
P6353
|
FINISHED |
| Object |
だるま市
だるま市は、縁起物の達磨を売る露店が並び、商売繁盛や合格祈願などを願う人々で賑わう日本各地の伝統的な市や祭りの総称です。
|
E1154938
|
NE FINISHED |
How this triple was built (4 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: だるま市 | Statement: [Daruma Market, hasLocalName, だるま市]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: だるま市 Context triple: [Daruma Market, hasLocalName, だるま市]
-
A.
Toda City
Toda City is a municipality in Saitama Prefecture, Japan, located just north of Tokyo and known as a residential and commuter town within the Greater Tokyo metropolitan area.
-
B.
Yuza Town
Yuza Town is a coastal municipality in northern Japan known for its scenic location in Yamagata Prefecture near Mount Chōkai and the Sea of Japan.
-
C.
Gotemba City
Gotemba City is a Japanese city in Shizuoka Prefecture near the southeastern base of Mount Fuji, known as a gateway for climbing and tourism around the mountain.
-
D.
Yamamoto Town
Yamamoto Town is a coastal municipality in northeastern Japan known for agriculture and for being heavily affected by the 2011 Tōhoku earthquake and tsunami.
-
E.
Nanyo City
Nanyo City is a municipality in northeastern Japan known for its hot springs, fruit production, and scenic rural landscapes.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: だるま市 Triple: [Daruma Market, hasLocalName, だるま市]
Generated description
だるま市は、縁起物の達磨を売る露店が並び、商売繁盛や合格祈願などを願う人々で賑わう日本各地の伝統的な市や祭りの総称です。
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: だるま市 Target entity description: だるま市は、縁起物の達磨を売る露店が並び、商売繁盛や合格祈願などを願う人々で賑わう日本各地の伝統的な市や祭りの総称です。
-
A.
Toda City
Toda City is a municipality in Saitama Prefecture, Japan, located just north of Tokyo and known as a residential and commuter town within the Greater Tokyo metropolitan area.
-
B.
Yuza Town
Yuza Town is a coastal municipality in northern Japan known for its scenic location in Yamagata Prefecture near Mount Chōkai and the Sea of Japan.
-
C.
Gotemba City
Gotemba City is a Japanese city in Shizuoka Prefecture near the southeastern base of Mount Fuji, known as a gateway for climbing and tourism around the mountain.
-
D.
Yamamoto Town
Yamamoto Town is a coastal municipality in northeastern Japan known for agriculture and for being heavily affected by the 2011 Tōhoku earthquake and tsunami.
-
E.
Nanyo City
Nanyo City is a municipality in northeastern Japan known for its hot springs, fruit production, and scenic rural landscapes.
- F. None of above. chosen
Provenance (5 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_69d85a16c68c819099c1b547fbc87b32 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e8d89e08190b7cae778d89fb5e1 |
completed | April 16, 2026, 1:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff13567e3481908eb6293c6af35f3a |
completed | May 9, 2026, 10:58 a.m. |
| NEDg | Description generation | batch_69ff144af00481909191a2d33874c195 |
completed | May 9, 2026, 11:02 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff15ae7c9c81909fd0894e48e5b5b1 |
completed | May 9, 2026, 11:08 a.m. |
Created at: April 10, 2026, 3:19 a.m.