Triple
T3530376
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shinagawa |
E74645
|
entity |
| Predicate | hasMajorArea |
P36071
|
FINISHED |
| Object |
Ōsaki
Ōsaki is a major commercial and business district in Tokyo known for its high-rise office complexes and convenient rail connections.
|
E689631
|
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: Ōsaki | Statement: [Shinagawa, hasMajorArea, Ōsaki]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ōsaki Context triple: [Shinagawa, hasMajorArea, Ōsaki]
-
A.
Takamatsu
Takamatsu is a coastal city in Japan’s Kagawa Prefecture on the island of Shikoku, known as a regional transport hub and gateway to the Seto Inland Sea.
-
B.
Kashihara
Kashihara is a city in Nara Prefecture, Japan, historically associated with the legendary founding of the Japanese imperial line and home to significant Shinto sites.
-
C.
Toyokawa
Toyokawa is a city in Aichi Prefecture, Japan, known for its historic Toyokawa Inari temple and manufacturing industries.
-
D.
Maibara
Maibara is a city in Shiga Prefecture, Japan, known as a regional transportation hub with a Shinkansen station and scenic views of nearby Lake Biwa and surrounding mountains.
-
E.
Kaizuka
Kaizuka is a coastal city in Osaka Prefecture, Japan, known for its historical temples, traditional festivals, and proximity to Osaka Bay.
- 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: Ōsaki Triple: [Shinagawa, hasMajorArea, Ōsaki]
Generated description
Ōsaki is a major commercial and business district in Tokyo known for its high-rise office complexes and convenient rail connections.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ōsaki Target entity description: Ōsaki is a major commercial and business district in Tokyo known for its high-rise office complexes and convenient rail connections.
-
A.
Takamatsu
Takamatsu is a coastal city in Japan’s Kagawa Prefecture on the island of Shikoku, known as a regional transport hub and gateway to the Seto Inland Sea.
-
B.
Kashihara
Kashihara is a city in Nara Prefecture, Japan, historically associated with the legendary founding of the Japanese imperial line and home to significant Shinto sites.
-
C.
Toyokawa
Toyokawa is a city in Aichi Prefecture, Japan, known for its historic Toyokawa Inari temple and manufacturing industries.
-
D.
Maibara
Maibara is a city in Shiga Prefecture, Japan, known as a regional transportation hub with a Shinkansen station and scenic views of nearby Lake Biwa and surrounding mountains.
-
E.
Kaizuka
Kaizuka is a coastal city in Osaka Prefecture, Japan, known for its historical temples, traditional festivals, and proximity to Osaka Bay.
- 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_69c902a459f481908dbba16de611b85a |
completed | March 29, 2026, 10:44 a.m. |
| NEDg | Description generation | batch_69c904629c18819085cc64d751780947 |
completed | March 29, 2026, 10:52 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c904c50e2c819084d43242ae8c10e2 |
completed | March 29, 2026, 10:53 a.m. |
Created at: March 8, 2026, 3:19 p.m.