Triple
T34036300
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shibasaki Station |
E872805
|
entity |
| Predicate | locatedInWardOrArea |
P76280
|
FINISHED |
| Object |
Shibasaki, Chōfu
Shibasaki, Chōfu is a neighborhood in the city of Chōfu, Tokyo, Japan, known as a residential area served by Shibasaki Station.
|
E2078703
|
NE FINISHED |
How this triple was built (3 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: Shibasaki, Chōfu | Statement: [Shibasaki Station, locatedInWardOrArea, Shibasaki, Chōfu]
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: Shibasaki, Chōfu Triple: [Shibasaki Station, locatedInWardOrArea, Shibasaki, Chōfu]
Generated description
Shibasaki, Chōfu is a neighborhood in the city of Chōfu, Tokyo, Japan, known as a residential area served by Shibasaki Station.
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: locatedInWardOrArea Context triple: [Shibasaki Station, locatedInWardOrArea, Shibasaki, Chōfu]
-
A.
locatedInCityWard
chosen
Indicates that one entity is situated within the administrative boundaries of a specific city ward.
-
B.
containsWard
Indicates that one entity includes, encompasses, or has within it a ward (such as a district, division, or dependent person) as part of its scope or responsibility.
-
C.
isWardOf
Indicates that one entity is under the guardianship, protection, or legal care of another entity as their ward.
-
D.
locatedInRegionalDistrict
Indicates that one entity is geographically situated within the boundaries of a specified regional district.
-
E.
locatedInLocalGovernmentUnit
Indicates that one entity is geographically or administratively situated within the boundaries of a specific local government unit.
- F. None of above.
Provenance (6 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_69f349a2527c81909a7cd4bda94d70ad |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_6a037c8ae0248190b7e2ce4bf852c22d |
completed | May 12, 2026, 7:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a36a03634408190973233703a87ee9a |
completed | June 20, 2026, 2:14 p.m. |
| NEDg | Description generation | batch_6a36a0c43d388190aa3499b2353aa31b |
completed | June 20, 2026, 2:16 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a36a16801c88190923bde7a7fd6a59d |
completed | June 20, 2026, 2:19 p.m. |
| PD | Predicate disambiguation | batch_6a0379f963908190846d232f386fd98f |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 1, 2026, 1:51 a.m.