Triple
T15335836
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Matsui Iwane |
E366662
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Iwane |
E366662
|
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: Iwane | Statement: [Matsui Iwane, givenName, Iwane]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Iwane Context triple: [Matsui Iwane, givenName, Iwane]
-
A.
Iwane
chosen
Iwane is a Japanese given name most notably borne by Matsui Iwane, a general of the Imperial Japanese Army during the early 20th century.
-
B.
Ikujiro
Ikujiro is a Japanese organizational theorist best known for his work on knowledge management and the SECI model of knowledge creation.
-
C.
Takayoshi
Takayoshi is a Japanese given name notably borne by Kido Takayoshi, a key samurai and statesman of the Meiji Restoration.
-
D.
Yasu
Yasu is a Japanese city located in Shiga Prefecture, known for its blend of residential areas, local industry, and proximity to Lake Biwa.
-
E.
Takaishi
Takaishi is a city in Osaka Prefecture, Japan, known as a small industrial and residential hub within the Osaka metropolitan area.
- 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_69d85a1355608190a6673ddb67231d54 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e03c5f081908e4d14dbdbc7f7a6 |
completed | April 16, 2026, 1:40 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff01f11b88819089342e8b088bc95e |
completed | May 9, 2026, 9:44 a.m. |
Created at: April 10, 2026, 3:17 a.m.