Triple
T19801573
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Iwakura Tomomi |
E475689
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Tomomi |
—
|
NE NERFINISHED |
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: Tomomi | Statement: [Iwakura Tomomi, givenName, Tomomi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tomomi Context triple: [Iwakura Tomomi, givenName, Tomomi]
-
A.
Tomomi
chosen
Tomomi is a Japanese given name that can be used for people of any gender.
-
B.
Tomoko
Tomoko is a common Japanese feminine given name that can have various meanings depending on the kanji characters used to write it.
-
C.
Tomoyuki
Tomoyuki is a Japanese masculine given name borne by various notable figures in fields such as the military, arts, and entertainment.
-
D.
Takako
Takako is a Japanese feminine given name borne by various notable figures in politics, arts, and entertainment.
-
E.
Tsutako
Tsutako is a Japanese given name, most notably borne by Tsutako Nakasone.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e51bc4208190a1c57d8c5d1b15e4 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e653cc995c81908e4ca85b0639d541 |
completed | April 20, 2026, 4:26 p.m. |
Created at: April 10, 2026, 1:49 p.m.