Triple
T12632302
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mode Gakuen Cocoon Tower |
E301672
|
entity |
| Predicate | houses |
P1643
|
FINISHED |
| Object |
Shuto Ikō
Shuto Ikō is a Japanese educational institution based in Tokyo, known for offering specialized vocational and professional training programs.
|
E1155461
|
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: Shuto Ikō | Statement: [Mode Gakuen Cocoon Tower, houses, Shuto Ikō]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shuto Ikō Context triple: [Mode Gakuen Cocoon Tower, houses, Shuto Ikō]
-
A.
Sakon Yamamoto
Sakon Yamamoto is a Japanese racing driver best known for his stint in Formula One during the mid-2000s with several backmarker teams.
-
B.
Saburō Kurusu
Saburō Kurusu was a Japanese diplomat best known for his role in U.S.-Japan negotiations immediately before the attack on Pearl Harbor.
-
C.
Tanaka Koki
Tanaka Koki is a Japanese entertainer best known as a former member of the popular boy band KAT-TUN.
-
D.
Kazuhiko
Kazuhiko is a Japanese given name commonly used for males.
-
E.
Yoshinori
Yoshinori is a Japanese given name commonly used for males.
- 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: Shuto Ikō Triple: [Mode Gakuen Cocoon Tower, houses, Shuto Ikō]
Generated description
Shuto Ikō is a Japanese educational institution based in Tokyo, known for offering specialized vocational and professional training programs.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Shuto Ikō Target entity description: Shuto Ikō is a Japanese educational institution based in Tokyo, known for offering specialized vocational and professional training programs.
-
A.
Sakon Yamamoto
Sakon Yamamoto is a Japanese racing driver best known for his stint in Formula One during the mid-2000s with several backmarker teams.
-
B.
Saburō Kurusu
Saburō Kurusu was a Japanese diplomat best known for his role in U.S.-Japan negotiations immediately before the attack on Pearl Harbor.
-
C.
Tanaka Koki
Tanaka Koki is a Japanese entertainer best known as a former member of the popular boy band KAT-TUN.
-
D.
Kazuhiko
Kazuhiko is a Japanese given name commonly used for males.
-
E.
Yoshinori
Yoshinori is a Japanese given name commonly used for males.
- 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_69d7bdec9f9c8190b4bac675b7588211 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9610e4f408190946f37325d69375c |
completed | April 10, 2026, 8:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff1a5a733c819090a6710ab990c38d |
completed | May 9, 2026, 11:28 a.m. |
| NEDg | Description generation | batch_69ff1afa99888190bfb60fd88d840d4e |
completed | May 9, 2026, 11:31 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff1bde8914819087d5d2ac88de34aa |
completed | May 9, 2026, 11:34 a.m. |
Created at: April 9, 2026, 5:15 p.m.