Triple
T9940960
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Masayuki |
E194078
|
entity |
| Predicate | romanizedAs |
P2508
|
FINISHED |
| Object | Masayuki |
E194078
|
NE FINISHED |
How this triple was built (2 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: Masayuki | Statement: [Masayuki, romanizedAs, Masayuki]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Masayuki Context triple: [Masayuki, romanizedAs, Masayuki]
-
A.
Masayuki
chosen
Masayuki is a Japanese given name commonly used for males.
-
B.
Kinnosuke
Kinnosuke is the given name of the renowned Japanese novelist Natsume Sōseki, a central figure in modern Japanese literature.
-
C.
Kenjirō
Kenjirō is a Japanese masculine given name that can be written with various kanji combinations and is borne by multiple notable individuals in fields such as sports, arts, and entertainment.
-
D.
Shinya
Shinya is a Japanese given name commonly used for males.
-
E.
Shintaro
Shintaro is a Japanese given name commonly used for males and borne by various notable figures in sports, entertainment, and politics.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69ca82e409348190a393777356b80a2a |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdb60f4ffc8190bfe916bb4a7bf5c5 |
completed | April 2, 2026, 12:19 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69dbb6bb34548190aac9d2af05477750 |
completed | April 12, 2026, 3:14 p.m. |
Created at: March 30, 2026, 8:44 p.m.