Triple
T1593741
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yukio Hatoyama |
E34232
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Yukio
Yukio is a Japanese given name commonly used for males and borne by several notable figures in politics, arts, and entertainment.
|
E260643
|
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: Yukio | Statement: [Yukio Hatoyama, givenName, Yukio]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yukio Context triple: [Yukio Hatoyama, givenName, Yukio]
-
A.
Yoshida
Yoshida is a common Japanese surname borne by numerous notable figures in politics, arts, sports, and entertainment.
-
B.
Kentarō
Kentarō is a Japanese given name commonly used for males, often associated with traditional or strong-sounding name combinations.
-
C.
Tanaka
Tanaka is a common Japanese surname borne by numerous notable figures in politics, arts, sports, and other fields.
-
D.
Nishiwaki
Nishiwaki is a city in central Hyōgo Prefecture, Japan, known for its location near the geographic center of the country and its mix of industrial and rural landscapes.
-
E.
Sakae
Sakae is a major downtown commercial and entertainment district in Nagoya, Japan, known for its shopping, nightlife, and landmark attractions.
- 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: Yukio Triple: [Yukio Hatoyama, givenName, Yukio]
Generated description
Yukio is a Japanese given name commonly used for males and borne by several notable figures in politics, arts, and entertainment.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Yukio Target entity description: Yukio is a Japanese given name commonly used for males and borne by several notable figures in politics, arts, and entertainment.
-
A.
Yoshida
Yoshida is a common Japanese surname borne by numerous notable figures in politics, arts, sports, and entertainment.
-
B.
Kentarō
Kentarō is a Japanese given name commonly used for males, often associated with traditional or strong-sounding name combinations.
-
C.
Tanaka
Tanaka is a common Japanese surname borne by numerous notable figures in politics, arts, sports, and other fields.
-
D.
Nishiwaki
Nishiwaki is a city in central Hyōgo Prefecture, Japan, known for its location near the geographic center of the country and its mix of industrial and rural landscapes.
-
E.
Sakae
Sakae is a major downtown commercial and entertainment district in Nagoya, Japan, known for its shopping, nightlife, and landmark attractions.
- 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_69a885fdcb9c819081ce6f0b8cd477dd |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a90929b32c8190be1a4b2d7b685735 |
completed | March 5, 2026, 4:40 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69aea82cd9748190ac82c7221455b2d5 |
completed | March 9, 2026, 10:59 a.m. |
| NEDg | Description generation | batch_69aeab510c908190b5c53ce49d18b880 |
completed | March 9, 2026, 11:13 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69aeac08f5248190893b5d378fde2836 |
completed | March 9, 2026, 11:16 a.m. |
Created at: March 4, 2026, 7:27 p.m.