Triple
T16081957
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kudan area |
E390130
|
entity |
| Predicate | hasSubarea |
P747
|
FINISHED |
| Object |
Kudan-higashi
Kudan-higashi is a district in Chiyoda, Tokyo, known for its central location near government institutions and historical sites.
|
E1458847
|
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: Kudan-higashi | Statement: [Kudan area, hasSubarea, Kudan-higashi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kudan-higashi Context triple: [Kudan area, hasSubarea, Kudan-higashi]
-
A.
Kudan-nishi
Kudan-nishi is a district in the Kudan area of Chiyoda, Tokyo, known for its central location near government offices and cultural landmarks.
-
B.
Kudamatsu
Kudamatsu is a coastal city in western Japan known for its industrial facilities and location along the Seto Inland Sea in Yamaguchi Prefecture.
-
C.
Higashikurume
Higashikurume is a suburban city in western Tokyo, Japan, known for its residential neighborhoods and role as a commuter area for central Tokyo.
-
D.
Kanramachi
Kanramachi is a Japanese town known for its cultural and municipal partnership with the Italian town of Certaldo.
-
E.
Kumagaya
Kumagaya is a city in northern Saitama Prefecture, Japan, known for its hot summer temperatures and role as a regional commercial and transportation hub.
- 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: Kudan-higashi Triple: [Kudan area, hasSubarea, Kudan-higashi]
Generated description
Kudan-higashi is a district in Chiyoda, Tokyo, known for its central location near government institutions and historical sites.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kudan-higashi Target entity description: Kudan-higashi is a district in Chiyoda, Tokyo, known for its central location near government institutions and historical sites.
-
A.
Kudan-nishi
Kudan-nishi is a district in the Kudan area of Chiyoda, Tokyo, known for its central location near government offices and cultural landmarks.
-
B.
Kudamatsu
Kudamatsu is a coastal city in western Japan known for its industrial facilities and location along the Seto Inland Sea in Yamaguchi Prefecture.
-
C.
Higashikurume
Higashikurume is a suburban city in western Tokyo, Japan, known for its residential neighborhoods and role as a commuter area for central Tokyo.
-
D.
Kanramachi
Kanramachi is a Japanese town known for its cultural and municipal partnership with the Italian town of Certaldo.
-
E.
Kumagaya
Kumagaya is a city in northern Saitama Prefecture, Japan, known for its hot summer temperatures and role as a regional commercial and transportation hub.
- 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_69d86daf32ec8190a8c0466c8f49c3c0 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1844b86288190ad0452aad6bdd5fb |
completed | April 17, 2026, 12:52 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a09276e98588190ad3bdb9d04a19584 |
completed | May 17, 2026, 2:26 a.m. |
| NEDg | Description generation | batch_6a0928a53e3c8190a8e636f5f1387f73 |
completed | May 17, 2026, 2:32 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0929485ebc8190ab8bc316b3e802ca |
completed | May 17, 2026, 2:34 a.m. |
Created at: April 10, 2026, 4:57 a.m.