Triple
T27891467
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bézout’s theorem |
E705365
|
entity |
| Predicate | countsIntersections |
P156511
|
FINISHED |
| Object | with multiplicity |
—
|
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: with multiplicity | Statement: [Bézout’s theorem, countsIntersections, with multiplicity]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: countsIntersections Context triple: [Bézout’s theorem, countsIntersections, with multiplicity]
-
A.
boardIntersectionCount
Indicates the number of distinct points or areas where two or more boards intersect or overlap.
-
B.
crossCount
chosen
Indicates the number of times one entity crosses or intersects another within a given context.
-
C.
crossesBetween
Indicates that one entity passes from one side of a second entity to the other, traversing the space between two reference points or boundaries associated with that second entity.
-
D.
isPlayedOnIntersections
Indicates that an activity or game takes place specifically at the intersection points of a defined grid or set of crossing lines.
-
E.
levelCrossingsApproximate
Indicates that one quantity or function has level crossings that approximately coincide with those of another, within some tolerance.
- 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_69ef96b39c448190a9b3aa6672a5168f |
completed | April 27, 2026, 5:02 p.m. |
| NER | Named-entity recognition | batch_69fdb31800508190beec15adb9bbd292 |
completed | May 8, 2026, 9:55 a.m. |
| PD | Predicate disambiguation | batch_69fdb19c381c8190bafb2f565da097f1 |
completed | May 8, 2026, 9:49 a.m. |
Created at: April 27, 2026, 6:36 p.m.