Triple
T19370519
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oka coherence theorem |
E484522
|
entity |
| Predicate | namedTheoremOf |
P29208
|
FINISHED |
| Object | Kiyoshi Oka |
—
|
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: Kiyoshi Oka | Statement: [Oka coherence theorem, namedTheoremOf, Kiyoshi Oka]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: namedTheoremOf Context triple: [Oka coherence theorem, namedTheoremOf, Kiyoshi Oka]
-
A.
hasTheorem
Indicates that one entity (typically a mathematical theory, field, or work) includes, establishes, or is associated with a particular theorem.
-
B.
hasTheoremNamedAfter
chosen
Indicates that a theorem is named in honor of or after a particular person or entity.
-
C.
relatedTheorem
Indicates that one theorem is connected to another through a logical, thematic, or derivational relationship.
-
D.
thesisOf
Indicates that a particular work is the thesis authored by a specified person or associated with a specified degree or institution.
-
E.
hasCorollary
Indicates that one statement, result, or proposition follows as a corollary from another, typically more general, statement.
- F. None of above.
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_69d8e8d305088190ad13571532aa454c |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e619af33e481908643f8beb2f498dc |
completed | April 20, 2026, 12:18 p.m. |
| PD | Predicate disambiguation | batch_69e4fd54f8e48190956e73dd8969164a |
completed | April 19, 2026, 4:05 p.m. |
Created at: April 10, 2026, 1:35 p.m.