Triple
T1767848
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Adachi |
E38804
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Kita-Senju
Kita-Senju is a major commercial and transportation hub in Tokyo, Japan, known for its busy railway station and shopping districts.
|
E207162
|
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: Kita-Senju | Statement: [Adachi, contains, Kita-Senju]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kita-Senju Context triple: [Adachi, contains, Kita-Senju]
-
A.
Gaimushō
Gaimushō is Japan’s Ministry of Foreign Affairs, responsible for managing the country’s diplomatic relations and international policies.
-
B.
Kizoku-in
Kizoku-in was the upper house of Japan’s prewar Imperial Diet, composed mainly of nobility and imperial appointees.
-
C.
Dogenzaka
Dogenzaka is a lively entertainment and shopping district in Shibuya, Tokyo, known for its nightlife, restaurants, and proximity to the famous Shibuya Crossing.
-
D.
To-ji
To-ji is a historic Buddhist temple in Kyoto, Japan, famed for its five-story pagoda—the tallest wooden tower in the country—and its status as a UNESCO World Heritage Site.
-
E.
Shiba-koen
Shiba-koen is a central Tokyo district known for its large public park, historic temples, and close proximity to Tokyo Tower.
- 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: Kita-Senju Triple: [Adachi, contains, Kita-Senju]
Generated description
Kita-Senju is a major commercial and transportation hub in Tokyo, Japan, known for its busy railway station and shopping districts.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kita-Senju Target entity description: Kita-Senju is a major commercial and transportation hub in Tokyo, Japan, known for its busy railway station and shopping districts.
-
A.
Gaimushō
Gaimushō is Japan’s Ministry of Foreign Affairs, responsible for managing the country’s diplomatic relations and international policies.
-
B.
Kizoku-in
Kizoku-in was the upper house of Japan’s prewar Imperial Diet, composed mainly of nobility and imperial appointees.
-
C.
Dogenzaka
Dogenzaka is a lively entertainment and shopping district in Shibuya, Tokyo, known for its nightlife, restaurants, and proximity to the famous Shibuya Crossing.
-
D.
To-ji
To-ji is a historic Buddhist temple in Kyoto, Japan, famed for its five-story pagoda—the tallest wooden tower in the country—and its status as a UNESCO World Heritage Site.
-
E.
Shiba-koen
Shiba-koen is a central Tokyo district known for its large public park, historic temples, and close proximity to Tokyo Tower.
- 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_69a8862e61708190af97b9838cc3f5de |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69aa648bb44c81909245fb7ee23cb132 |
completed | March 6, 2026, 5:22 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69add1b679d88190b3c6e50c96f917e4 |
completed | March 8, 2026, 7:44 p.m. |
| NEDg | Description generation | batch_69add246f1a88190b3e14d1e45f5d433 |
completed | March 8, 2026, 7:47 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69add2afe284819083723ccaa2219222 |
completed | March 8, 2026, 7:49 p.m. |
Created at: March 4, 2026, 7:31 p.m.