Triple
T33578663
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Seiberg–Witten differential |
E860093
|
entity |
| Predicate | periodsGive |
P180094
|
FINISHED |
| Object | electric charges |
—
|
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: electric charges | Statement: [Seiberg–Witten differential, periodsGive, electric charges]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: periodsGive Context triple: [Seiberg–Witten differential, periodsGive, electric charges]
-
A.
typicalPeriod
Indicates the usual or characteristic time interval or duration associated with an event, process, or state.
-
B.
numberOfPeriods
Indicates the count of distinct time intervals or periods associated with an entity or event.
-
C.
showsTimePeriod
Indicates that one entity presents or displays a specific span or interval of time associated with another entity.
-
D.
periodProperty
Indicates a temporal characteristic or attribute associated with a specific time period or interval.
-
E.
periodizedAs
Indicates that something has been divided or organized into distinct time periods according to a particular periodization scheme.
- 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_69f3497d37848190afcbb5ef3f5c7376 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f7308a096081909d66a56f3c926806 |
completed | May 3, 2026, 11:24 a.m. |
| PD | Predicate disambiguation | batch_69f72a00c5f081908b6539d15baf4e12 |
completed | May 3, 2026, 10:57 a.m. |
| PDg | Predicate description generation | batch_69f730890a008190a882f7828f1c9162 |
completed | May 3, 2026, 11:24 a.m. |
Created at: May 1, 2026, 1:40 a.m.