Triple
T18956168
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kummer surface |
E463785
|
entity |
| Predicate | numberOf2TorsionPoints |
P133944
|
FINISHED |
| Object | 16 |
—
|
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: 16 | Statement: [Kummer surface, numberOf2TorsionPoints, 16]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOf2TorsionPoints Context triple: [Kummer surface, numberOf2TorsionPoints, 16]
-
A.
hasNumberOfCusps
Indicates the specific count of cusps (pointed projections or tips) that an entity possesses.
-
B.
hasTorsion
Indicates that an object or structure possesses torsion, meaning it is subject to or characterized by twisting about an axis.
-
C.
trivialRationalPoints
Indicates that the only rational points satisfying the given condition or lying on the given object are the trivial or obvious ones (typically those forced by the structure, such as points at infinity or simple coordinate values).
-
D.
numberOfPairsUsed
Indicates the quantity of distinct pairs involved or utilized in a given context or operation.
-
E.
twistCount
Indicates the number of twists or rotational turns applied or present in the relationship between the related entities.
- 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_69d8dcffc278819086792a4ebfddfafa |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5d5cdf2d08190a0aecd3fa5335a75 |
completed | April 20, 2026, 7:29 a.m. |
| PD | Predicate disambiguation | batch_69e4a2f437648190b85650dae8885d48 |
completed | April 19, 2026, 9:40 a.m. |
| PDg | Predicate description generation | batch_69e4ad8e075c8190ad561edc5e520057 |
completed | April 19, 2026, 10:25 a.m. |
Created at: April 10, 2026, noon