Triple
T6274783
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gyo Obata |
E140628
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Gyo
Gyo is the given name of Gyo Obata, a prominent American architect known for designing major cultural and civic buildings.
|
E598152
|
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: Gyo | Statement: [Gyo Obata, givenName, Gyo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gyo Context triple: [Gyo Obata, givenName, Gyo]
-
A.
Gyoda
Gyoda is a historic city in eastern Japan known for its ancient rice paddies, traditional tabi sock production, and preserved castle town atmosphere.
-
B.
Kōgō
Kōgō is the Japanese term used to refer to the empress consort of Japan.
-
C.
Miyazya
Miyazya is one of the spring months in the Ethiopian calendar, roughly corresponding to April in the Gregorian calendar.
-
D.
Togoshi
Togoshi is a residential and commercial neighborhood in Tokyo’s Shinagawa ward, known for its traditional shopping streets and local atmosphere.
-
E.
Shiga
Shiga is a landlocked prefecture in central Japan known for encompassing Lake Biwa, the country’s largest freshwater lake, and for its historical sites and natural scenery.
- 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: Gyo Triple: [Gyo Obata, givenName, Gyo]
Generated description
Gyo is the given name of Gyo Obata, a prominent American architect known for designing major cultural and civic buildings.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Gyo Target entity description: Gyo is the given name of Gyo Obata, a prominent American architect known for designing major cultural and civic buildings.
-
A.
Gyoda
Gyoda is a historic city in eastern Japan known for its ancient rice paddies, traditional tabi sock production, and preserved castle town atmosphere.
-
B.
Kōgō
Kōgō is the Japanese term used to refer to the empress consort of Japan.
-
C.
Miyazya
Miyazya is one of the spring months in the Ethiopian calendar, roughly corresponding to April in the Gregorian calendar.
-
D.
Togoshi
Togoshi is a residential and commercial neighborhood in Tokyo’s Shinagawa ward, known for its traditional shopping streets and local atmosphere.
-
E.
Shiga
Shiga is a landlocked prefecture in central Japan known for encompassing Lake Biwa, the country’s largest freshwater lake, and for its historical sites and natural scenery.
- 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_69c008cc158881908df6ec94a911c736 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c063c0629c8190805ddf1a604e9ca4 |
completed | March 22, 2026, 9:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c669d6ff748190b77d5a2c9cbe506b |
completed | March 27, 2026, 11:28 a.m. |
| NEDg | Description generation | batch_69c66ba76c7881908fac8eb372efa08c |
completed | March 27, 2026, 11:36 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c66c1e32648190b4e584d144ad94ff |
completed | March 27, 2026, 11:38 a.m. |
Created at: March 22, 2026, 4:25 p.m.