Triple
T6908929
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zariski topology |
E159881
|
entity |
| Predicate | specializationOrder |
P74069
|
FINISHED |
| Object | inclusion order on prime ideals |
—
|
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: inclusion order on prime ideals | Statement: [Zariski topology, specializationOrder, inclusion order on prime ideals]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: specializationOrder Context triple: [Zariski topology, specializationOrder, inclusion order on prime ideals]
-
A.
positionSpecialization
Indicates that one position is a more specialized or focused variant of another, broader position.
-
B.
componentOrder
Indicates the relative sequence or arrangement of components within a larger structure or system.
-
C.
selectionOrderAdjustment
Indicates a modification to the original order in which items or options were selected, such as reordering or adjusting their selection sequence.
-
D.
uniformSpecialty
Indicates that multiple entities share the same specific specialty, expertise, or area of focus.
-
E.
formatSpecialization
Indicates a relationship where one format is a specialized or more specific version of another, more general format.
- F. None of above. chosen
Provenance (4 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_69c68839ccb88190b4aa5cc1aca3448f |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6d9be98748190b5cb698e66e3aa42 |
completed | March 27, 2026, 7:25 p.m. |
| PD | Predicate disambiguation | batch_69c6d7b93d688190a297244ce81b67ac |
completed | March 27, 2026, 7:17 p.m. |
| PDg | Predicate description generation | batch_69c6d8c48ba48190b8d3aa7b8d22816b |
completed | March 27, 2026, 7:21 p.m. |
Created at: March 27, 2026, 2:25 p.m.