Triple
T1101770
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Taro Aso |
E24395
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Taro |
E33651
|
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: Taro | Statement: [Taro Aso, givenName, Taro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Taro Context triple: [Taro Aso, givenName, Taro]
-
A.
Taro
chosen
Taro is a common Japanese male given name, often written with kanji meaning "eldest son" or similar traditional connotations.
-
B.
Maki
Maki is a Japanese surname most notably associated with Fumihiko Maki, a prominent modernist architect known for his innovative urban and architectural designs.
-
C.
Tamada
Tamada is the traditional Georgian toastmaster who leads feasts and orchestrates toasts during the supra, Georgia’s ceremonial banquet.
-
D.
Tama
Tama is a region in western Tokyo, Japan, encompassing several suburban cities and towns that serve as residential and commercial areas for the greater Tokyo metropolis.
-
E.
Oshiwambo
Oshiwambo is a Bantu language (or cluster of closely related dialects) widely spoken by the Ovambo people in northern Namibia and southern Angola.
- 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_69a4940542308190ac2a0b1f730b7cfc |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4b9c21c2c8190a34d91a7afed23a9 |
completed | March 1, 2026, 10:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac66293b5c819091eb69db328d5698 |
completed | March 7, 2026, 5:53 p.m. |
Created at: March 1, 2026, 7:43 p.m.