Triple
T12357686
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Genius: A Mosaic of One Hundred Exemplary Creative Minds |
E294652
|
entity |
| Predicate | numberOfSubjectsDiscussed |
P101171
|
FINISHED |
| Object | 100 |
—
|
LITERAL 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: 100 | Statement: [Genius: A Mosaic of One Hundred Exemplary Creative Minds, numberOfSubjectsDiscussed, 100]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfSubjectsDiscussed Context triple: [Genius: A Mosaic of One Hundred Exemplary Creative Minds, numberOfSubjectsDiscussed, 100]
-
A.
subjectCount
chosen
Indicates the number of subjects associated with or involved in a given entity or context.
-
B.
numberOfLectures
Indicates the total count of lectures associated with a given entity or context.
-
C.
numberOfDialogues
Indicates the total count of dialogues associated with or occurring between the referenced entities.
-
D.
numberOfCourses
Indicates the quantity of courses associated with a given entity.
-
E.
numberOfQuestions
Indicates the total count of questions associated with or contained in a given entity or context.
- 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_69d6ab6ccbec8190b09e2d357aa80064 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d942a2d6e08190a13c7ff89af09354 |
completed | April 10, 2026, 6:34 p.m. |
| PD | Predicate disambiguation | batch_69d93ecf6b548190a394b6b56a0c1c68 |
completed | April 10, 2026, 6:17 p.m. |
Created at: April 8, 2026, 9:54 p.m.