Triple
T10474370
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Great Whirl |
E247006
|
entity |
| Predicate | hasTemporalBehavior |
P94226
|
FINISHED |
| Object | interannual variability |
—
|
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: interannual variability | Statement: [Great Whirl, hasTemporalBehavior, interannual variability]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTemporalBehavior Context triple: [Great Whirl, hasTemporalBehavior, interannual variability]
-
A.
hasTemporalUse
Indicates that something is used, applicable, or valid only during a specific time or temporal interval.
-
B.
hasTemporalRole
Indicates that an entity participates in a role or function that is defined, constrained, or characterized by a specific time or temporal context.
-
C.
hasTemporalResolution
Indicates that one entity specifies the level of temporal detail or granularity at which another entity’s data, observation, or process is measured or represented.
-
D.
hasTemporalClassification
Indicates a relationship where something is assigned or associated with a specific temporal category, period, or time-based classification.
-
E.
hasTemporalLocation
Indicates that something occurs, exists, or is valid during a specific time or time interval.
- 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_69d381c16c248190a2fe5b471e584e9c |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d5094e74048190a2c70ef32c50ba71 |
completed | April 7, 2026, 1:40 p.m. |
| PD | Predicate disambiguation | batch_69d4fb84bafc8190819757b93620508a |
completed | April 7, 2026, 12:41 p.m. |
| PDg | Predicate description generation | batch_69d4fe058fcc81909428137d9ffd6d90 |
completed | April 7, 2026, 12:52 p.m. |
Created at: April 6, 2026, 12:21 p.m.