Triple
T3877544
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Satō |
E92538
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Miki Satō
Miki Satō is a Japanese singer-songwriter known for her emotionally expressive vocals and contributions to anime theme songs.
|
E408981
|
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: Miki Satō | Statement: [Satō, hasNotableBearer, Miki Satō]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Miki Satō Context triple: [Satō, hasNotableBearer, Miki Satō]
-
A.
Yuki Satō
Yuki Satō is a Japanese name shared by multiple notable individuals, including athletes and entertainers, distinguished in their respective fields.
-
B.
Chieko Mori
Chieko Mori is a Japanese woman best known as the wife of former Prime Minister Yoshirō Mori.
-
C.
Kiko Mizuhara
Kiko Mizuhara is a Japanese-American model, actress, and designer known for her prominent work in fashion and film, as well as her influence in contemporary Japanese pop culture.
-
D.
Lisa Matsuda
Lisa Matsuda is a neuroscientist best known for identifying and characterizing the CB1 cannabinoid receptor, a key component of the endocannabinoid system in the brain.
-
E.
Naoko Mori
Naoko Mori is a Japanese-born British actress best known for her roles in the TV series "Torchwood" and the musical "Miss Saigon."
- 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: Miki Satō Triple: [Satō, hasNotableBearer, Miki Satō]
Generated description
Miki Satō is a Japanese singer-songwriter known for her emotionally expressive vocals and contributions to anime theme songs.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Miki Satō Target entity description: Miki Satō is a Japanese singer-songwriter known for her emotionally expressive vocals and contributions to anime theme songs.
-
A.
Yuki Satō
Yuki Satō is a Japanese name shared by multiple notable individuals, including athletes and entertainers, distinguished in their respective fields.
-
B.
Chieko Mori
Chieko Mori is a Japanese woman best known as the wife of former Prime Minister Yoshirō Mori.
-
C.
Kiko Mizuhara
Kiko Mizuhara is a Japanese-American model, actress, and designer known for her prominent work in fashion and film, as well as her influence in contemporary Japanese pop culture.
-
D.
Lisa Matsuda
Lisa Matsuda is a neuroscientist best known for identifying and characterizing the CB1 cannabinoid receptor, a key component of the endocannabinoid system in the brain.
-
E.
Naoko Mori
Naoko Mori is a Japanese-born British actress best known for her roles in the TV series "Torchwood" and the musical "Miss Saigon."
- 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_69aed967448c819086c4b358d37b25aa |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeec72fa7c81909c73b3cf90597e9a |
completed | March 9, 2026, 3:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b55612ab7081909ab9375da1e4119b |
completed | March 14, 2026, 12:35 p.m. |
| NEDg | Description generation | batch_69b55718acb88190a491e9654c1f1b7f |
completed | March 14, 2026, 12:39 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b55785a6b4819083737f26eb5db217 |
completed | March 14, 2026, 12:41 p.m. |
Created at: March 9, 2026, 3:20 p.m.