Triple
T3499708
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kashio Seisakujo |
E73934
|
entity |
| Predicate | evolvedInto |
P1245
|
FINISHED |
| Object | Casio |
E13780
|
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: Casio | Statement: [Kashio Seisakujo, evolvedInto, Casio]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Casio Context triple: [Kashio Seisakujo, evolvedInto, Casio]
-
A.
Casio
chosen
Casio is a Japanese electronics company best known for its durable digital watches, calculators, and consumer electronics.
-
B.
Seiko
Seiko is a Japanese given name commonly used for women and borne by various notable figures in politics, entertainment, and sports.
-
C.
Panasonic
Panasonic is a major Japanese multinational electronics company known for its wide range of consumer electronics, home appliances, and industrial solutions.
-
D.
Busicom
Busicom was a Japanese calculator and electronics company best known for commissioning the Intel 4004, the first commercial microprocessor.
-
E.
Tandy
Tandy is a surname most notably associated with Jessica Tandy, the acclaimed British-American actress known for her work on stage and in film.
- 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_69ad85cdb6e48190a335d412b9194ed8 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbbd4eb308190b84e84261ceec229 |
completed | March 8, 2026, 6:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4c3718bd88190bec2f4cfda0010ae |
completed | March 14, 2026, 2:09 a.m. |
Created at: March 8, 2026, 3:18 p.m.