Triple
T262029
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tokyo |
E5560
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Taitō
Taitō is a special ward in central Tokyo known for its historic districts, traditional temples, and major cultural attractions such as Ueno Park and Asakusa.
|
E34802
|
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: Taitō | Statement: [Tokyo, contains, Taitō]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Taitō Context triple: [Tokyo, contains, Taitō]
-
A.
Inari Ōkami
Inari Ōkami is a major Shinto kami associated primarily with rice, agriculture, prosperity, and fox spirits, widely revered at thousands of shrines across Japan.
-
B.
Tikkana
Tikkana was a prominent 13th-century Telugu poet and scholar best known for translating a major portion of the Mahabharata into Telugu and helping shape classical Telugu literature.
-
C.
Mario
Mario is a fictional Italian plumber and the iconic protagonist of Nintendo's long-running Super Mario video game franchise.
-
D.
Namba Grand Kagetsu
Namba Grand Kagetsu is a famous comedy theater in Osaka, Japan, known as a central venue for Yoshimoto Kogyo’s manzai and variety performances.
-
E.
Tora
Tora is a popular nickname for the Hanshin Tigers, a professional Japanese baseball team based in the Kansai region.
- 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: Taitō Triple: [Tokyo, contains, Taitō]
Generated description
Taitō is a special ward in central Tokyo known for its historic districts, traditional temples, and major cultural attractions such as Ueno Park and Asakusa.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Taitō Target entity description: Taitō is a special ward in central Tokyo known for its historic districts, traditional temples, and major cultural attractions such as Ueno Park and Asakusa.
-
A.
Inari Ōkami
Inari Ōkami is a major Shinto kami associated primarily with rice, agriculture, prosperity, and fox spirits, widely revered at thousands of shrines across Japan.
-
B.
Tikkana
Tikkana was a prominent 13th-century Telugu poet and scholar best known for translating a major portion of the Mahabharata into Telugu and helping shape classical Telugu literature.
-
C.
Mario
Mario is a fictional Italian plumber and the iconic protagonist of Nintendo's long-running Super Mario video game franchise.
-
D.
Namba Grand Kagetsu
Namba Grand Kagetsu is a famous comedy theater in Osaka, Japan, known as a central venue for Yoshimoto Kogyo’s manzai and variety performances.
-
E.
Tora
Tora is a popular nickname for the Hanshin Tigers, a professional Japanese baseball team based in the Kansai region.
- 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_69a2580a64ac8190ad76e34bb0715b5e |
completed | Feb. 28, 2026, 2:50 a.m. |
| NER | Named-entity recognition | batch_69a25d7428dc8190ae12b12a21fcc6cb |
completed | Feb. 28, 2026, 3:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a38b8ca1f08190a13733c03b9161f9 |
completed | March 1, 2026, 12:42 a.m. |
| NEDg | Description generation | batch_69a38bcc7d3c8190a2a1216f58631c5c |
completed | March 1, 2026, 12:43 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a38c56810c8190922ad62014393734 |
completed | March 1, 2026, 12:46 a.m. |
Created at: Feb. 28, 2026, 2:55 a.m.