Triple
T12414019
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 民進党 |
E296589
|
entity |
| Predicate | headquartersLocation |
P62
|
FINISHED |
| Object |
永田町
永田町 is a district in Tokyo’s Chiyoda Ward that serves as Japan’s political center, housing key government institutions and major party headquarters.
|
E981175
|
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: 永田町 | Statement: [民進党, headquartersLocation, 永田町]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 永田町 Context triple: [民進党, headquartersLocation, 永田町]
-
A.
瑞穂町
瑞穂町は、日本の東京都西多摩郡に位置する住宅地と自然が混在する町です。
-
B.
京田辺市
京田辺市は、京都府南部に位置し、同志社大学のキャンパスなどを擁する住宅都市・学園都市として知られる市です。
-
C.
交野市
交野市は、大阪府北河内地域に位置する自然豊かな住宅都市で、星田妙見宮や天野川などで知られる市です。
-
D.
高千穂町
高千穂町は、宮崎県北西部に位置し、神話の里として知られる峡谷や高千穂神社などの観光名所で有名な町です。
-
E.
豊郷町
豊郷町は、滋賀県犬上郡に位置し、アニメ『けいおん!』の舞台モデルとして知られる小さな町です。
- 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: 永田町 Triple: [民進党, headquartersLocation, 永田町]
Generated description
永田町 is a district in Tokyo’s Chiyoda Ward that serves as Japan’s political center, housing key government institutions and major party headquarters.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: 永田町 Target entity description: 永田町 is a district in Tokyo’s Chiyoda Ward that serves as Japan’s political center, housing key government institutions and major party headquarters.
-
A.
瑞穂町
瑞穂町は、日本の東京都西多摩郡に位置する住宅地と自然が混在する町です。
-
B.
京田辺市
京田辺市は、京都府南部に位置し、同志社大学のキャンパスなどを擁する住宅都市・学園都市として知られる市です。
-
C.
交野市
交野市は、大阪府北河内地域に位置する自然豊かな住宅都市で、星田妙見宮や天野川などで知られる市です。
-
D.
高千穂町
高千穂町は、宮崎県北西部に位置し、神話の里として知られる峡谷や高千穂神社などの観光名所で有名な町です。
-
E.
豊郷町
豊郷町は、滋賀県犬上郡に位置し、アニメ『けいおん!』の舞台モデルとして知られる小さな町です。
- 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_69d6ad9f464c81909db36d7e96e34b9e |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94d6c4f6c8190bc99d3f7b64205c3 |
completed | April 10, 2026, 7:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6348ccaf88190aeb0dfb7fe1d8dec |
completed | May 2, 2026, 5:29 p.m. |
| NEDg | Description generation | batch_69f635997b088190b6207fcac5594eb2 |
completed | May 2, 2026, 5:34 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f636d9e13881908d3d08c6cf954304 |
completed | May 2, 2026, 5:39 p.m. |
Created at: April 8, 2026, 9:55 p.m.