Triple
T11084463
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dynkin formula |
E262081
|
entity |
| Predicate | timeDomain |
P97135
|
FINISHED |
| Object | continuous time |
—
|
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: continuous time | Statement: [Dynkin formula, timeDomain, continuous time]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: timeDomain Context triple: [Dynkin formula, timeDomain, continuous time]
-
A.
timePeriod
Indicates the specific span or interval of time during which an event, state, or relationship occurs or is valid.
-
B.
timeType
Indicates the specific temporal category or classification associated with a time-related entity or value (e.g., duration, point in time, interval, or recurrence type).
-
C.
timeSystem
Indicates a relationship where an entity uses, follows, or is defined within a particular system for measuring or organizing time.
-
D.
timeScaleCategory
Indicates the classification of an event or process based on the temporal scale or duration over which it occurs.
-
E.
dimensionOfTime
Indicates a temporal measurement or extent that specifies how long something lasts or when it occurs.
- 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_69d6aa9983c08190b0ef61603b69feac |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d799c0cc3081908448cfb26c08daf5 |
completed | April 9, 2026, 12:21 p.m. |
| PD | Predicate disambiguation | batch_69d744185a5881909ba4cf151d1798ec |
completed | April 9, 2026, 6:15 a.m. |
| PDg | Predicate description generation | batch_69d750ca52ec8190a559432a5de106fd |
completed | April 9, 2026, 7:10 a.m. |
Created at: April 8, 2026, 9:27 p.m.