Triple
T1483894
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Conway polynomial |
E29419
|
entity |
| Predicate | firstCoefficientProperty |
P29150
|
FINISHED |
| Object | constant term is 1 for knots |
—
|
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: constant term is 1 for knots | Statement: [Conway polynomial, firstCoefficientProperty, constant term is 1 for knots]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: firstCoefficientProperty Context triple: [Conway polynomial, firstCoefficientProperty, constant term is 1 for knots]
-
A.
firstOf
Indicates that one entity is the earliest or initial member in an ordered sequence or collection relative to the others.
-
B.
firstSeriesCode
Indicates that an entity is identified as the initial or primary series within a sequence, referenced by a specific code.
-
C.
firstWord
Indicates that one entity is the first word in the sequence or text associated with another entity.
-
D.
firstModel
Indicates that an entity is the initial or earliest model/version in a sequence or series of models.
-
E.
firstTier
Indicates that one entity occupies the highest or primary level, rank, or priority relative to others in a hierarchical structure.
- 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_69a498da82e08190ba833330d05f380f |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c679714c8190ac53630fb49e19c5 |
completed | March 1, 2026, 11:06 p.m. |
| PD | Predicate disambiguation | batch_69a4c486eacc81909c272f9bdf50a7c3 |
completed | March 1, 2026, 10:58 p.m. |
| PDg | Predicate description generation | batch_69a4c52bbb748190aaa804438d31f4c2 |
completed | March 1, 2026, 11 p.m. |
Created at: March 1, 2026, 8:12 p.m.