Triple
T14758970
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Junko Noda |
E346804
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Junko
Junko is a common Japanese feminine given name borne by numerous notable figures in fields such as entertainment, sports, and the arts.
|
E1119545
|
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: Junko | Statement: [Junko Noda, givenName, Junko]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Junko Context triple: [Junko Noda, givenName, Junko]
-
A.
Sachiko
Sachiko is a Japanese feminine given name that can be written with various kanji combinations, often conveying meanings related to happiness or child.
-
B.
Yuriko
Yuriko is the given name of Japanese actress Rinko Kikuchi, known for her roles in films such as "Babel" and "Pacific Rim."
-
C.
Kiyoko
Kiyoko is a Japanese feminine given name that can be written with various kanji combinations, often carrying meanings related to purity or respect.
-
D.
Yoshiko
Yoshiko is a feminine Japanese given name commonly used across various generations and often associated with traditional Japanese culture.
-
E.
Kazuko
Kazuko is a Japanese feminine given name commonly borne by women, including members of the imperial family.
- 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: Junko Triple: [Junko Noda, givenName, Junko]
Generated description
Junko is a common Japanese feminine given name borne by numerous notable figures in fields such as entertainment, sports, and the arts.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Junko Target entity description: Junko is a common Japanese feminine given name borne by numerous notable figures in fields such as entertainment, sports, and the arts.
-
A.
Sachiko
Sachiko is a Japanese feminine given name that can be written with various kanji combinations, often conveying meanings related to happiness or child.
-
B.
Yuriko
Yuriko is the given name of Japanese actress Rinko Kikuchi, known for her roles in films such as "Babel" and "Pacific Rim."
-
C.
Kiyoko
Kiyoko is a Japanese feminine given name that can be written with various kanji combinations, often carrying meanings related to purity or respect.
-
D.
Yoshiko
Yoshiko is a feminine Japanese given name commonly used across various generations and often associated with traditional Japanese culture.
-
E.
Kazuko
Kazuko is a Japanese feminine given name commonly borne by women, including members of the imperial family.
- 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_69d822e8896c819091169882f9b20486 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dec7f0f5a48190af008352c26574d7 |
completed | April 14, 2026, 11:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe0cefb7c08190bf69b15165f046d0 |
completed | May 8, 2026, 4:18 p.m. |
| NEDg | Description generation | batch_69fe1a0e2820819092abfc2795ba4851 |
completed | May 8, 2026, 5:14 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fe1a89d4d08190a2be8b4b0bc5a472 |
completed | May 8, 2026, 5:16 p.m. |
Created at: April 10, 2026, 1:30 a.m.