Triple
T3530382
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shinagawa |
E74645
|
entity |
| Predicate | hasMajorArea |
P36071
|
FINISHED |
| Object |
Hatanodai
Hatanodai is a residential neighborhood in Tokyo, Japan, known for its local shopping streets and convenient rail connections.
|
E370034
|
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: Hatanodai | Statement: [Shinagawa, hasMajorArea, Hatanodai]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hatanodai Context triple: [Shinagawa, hasMajorArea, Hatanodai]
-
A.
Akiruno
Akiruno is a city in western Tokyo, Japan, known for its natural scenery, including rivers, forests, and hiking areas.
-
B.
Omotesandō
Omotesandō is a fashionable, tree-lined avenue in Tokyo known for its high-end boutiques, modern architecture, and trendy cafés.
-
C.
Tomakomai
Tomakomai is an industrial port city on the southern coast of Hokkaido, Japan, known for its paper manufacturing, shipping, and ferry connections.
-
D.
Higashikurume
Higashikurume is a suburban city in western Tokyo, Japan, known for its residential neighborhoods and role as a commuter area for central Tokyo.
-
E.
Atami
Atami is a coastal hot spring resort city in Shizuoka Prefecture, Japan, known for its onsen, beaches, and proximity to Tokyo.
- 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: Hatanodai Triple: [Shinagawa, hasMajorArea, Hatanodai]
Generated description
Hatanodai is a residential neighborhood in Tokyo, Japan, known for its local shopping streets and convenient rail connections.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hatanodai Target entity description: Hatanodai is a residential neighborhood in Tokyo, Japan, known for its local shopping streets and convenient rail connections.
-
A.
Akiruno
Akiruno is a city in western Tokyo, Japan, known for its natural scenery, including rivers, forests, and hiking areas.
-
B.
Omotesandō
Omotesandō is a fashionable, tree-lined avenue in Tokyo known for its high-end boutiques, modern architecture, and trendy cafés.
-
C.
Tomakomai
Tomakomai is an industrial port city on the southern coast of Hokkaido, Japan, known for its paper manufacturing, shipping, and ferry connections.
-
D.
Higashikurume
Higashikurume is a suburban city in western Tokyo, Japan, known for its residential neighborhoods and role as a commuter area for central Tokyo.
-
E.
Atami
Atami is a coastal hot spring resort city in Shizuoka Prefecture, Japan, known for its onsen, beaches, and proximity to Tokyo.
- 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_69ad85d1a3948190931fd1ea1f49717b |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbc9764a881908aa8d25dc9adf59e |
completed | March 8, 2026, 6:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b3bb8421b481908bda2b4f45714605 |
completed | March 13, 2026, 7:23 a.m. |
| NEDg | Description generation | batch_69b3bcd930a881908156e7857af4d059 |
completed | March 13, 2026, 7:29 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b3f68512a08190b88b283c760ed3d2 |
completed | March 13, 2026, 11:35 a.m. |
Created at: March 8, 2026, 3:19 p.m.