Triple
T3561545
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shohei Ohtani |
E75349
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Shohei
Shohei is a Japanese given name most prominently associated with baseball star Shohei Ohtani.
|
E375532
|
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: Shohei | Statement: [Shohei Ohtani, givenName, Shohei]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shohei Context triple: [Shohei Ohtani, givenName, Shohei]
-
A.
Akinobu
Akinobu is a Japanese masculine given name that can be written with various kanji combinations and is borne by several notable individuals.
-
B.
Min Tanaka
Min Tanaka is a renowned Japanese dancer and actor known for his avant-garde butoh performances and roles in international films.
-
C.
Matsui
Matsui is a Japanese surname borne by various notable figures in fields such as politics, sports, and the military.
-
D.
Tatsunori Hara
Tatsunori Hara is a prominent Japanese baseball manager and former Yomiuri Giants star known for leading both his club and Japan’s national team to multiple championships.
-
E.
Yasuo Matsui
Yasuo Matsui was a Japanese-American architect active in early 20th-century New York City, known for his work on prominent skyscrapers.
- 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: Shohei Triple: [Shohei Ohtani, givenName, Shohei]
Generated description
Shohei is a Japanese given name most prominently associated with baseball star Shohei Ohtani.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Shohei Target entity description: Shohei is a Japanese given name most prominently associated with baseball star Shohei Ohtani.
-
A.
Akinobu
Akinobu is a Japanese masculine given name that can be written with various kanji combinations and is borne by several notable individuals.
-
B.
Min Tanaka
Min Tanaka is a renowned Japanese dancer and actor known for his avant-garde butoh performances and roles in international films.
-
C.
Matsui
Matsui is a Japanese surname borne by various notable figures in fields such as politics, sports, and the military.
-
D.
Tatsunori Hara
Tatsunori Hara is a prominent Japanese baseball manager and former Yomiuri Giants star known for leading both his club and Japan’s national team to multiple championships.
-
E.
Yasuo Matsui
Yasuo Matsui was a Japanese-American architect active in early 20th-century New York City, known for his work on prominent skyscrapers.
- 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_69ad85d45090819086f34fb85d850a1e |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc08ac1b0819082021ecdd0061dc3 |
completed | March 8, 2026, 6:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b44ef5f5348190b1088edee0d9d55c |
completed | March 13, 2026, 5:52 p.m. |
| NEDg | Description generation | batch_69b45285db388190b437b32db0bd4322 |
completed | March 13, 2026, 6:08 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b462b64d2c81909683f8aeb1c0e924 |
completed | March 13, 2026, 7:17 p.m. |
Created at: March 8, 2026, 3:21 p.m.