Triple
T22998671
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Polar Circles |
E572577
|
entity |
| Predicate | markRegionWith |
P63197
|
FINISHED |
| Object | at least one day of continuous daylight per year |
—
|
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: at least one day of continuous daylight per year | Statement: [Polar Circles, markRegionWith, at least one day of continuous daylight per year]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: markRegionWith Context triple: [Polar Circles, markRegionWith, at least one day of continuous daylight per year]
-
A.
marksBoundaryIn
Indicates that one entity serves as a boundary or delimiter within, or for a specific region or structure of, another entity.
-
B.
trackRegion
Indicates that an entity is associated with, occurs within, or is relevant to a specific geographic or logical region used for tracking.
-
C.
markingFeature
Indicates a feature that serves as a distinguishing mark or identifier associated with an entity.
-
D.
marksOn
Indicates that one entity bears visible signs, traces, or imprints that have been made or left by another entity.
-
E.
labelRegion
chosen
Indicates assigning a categorical or descriptive label to a specified region or area.
- F. None of above.
Provenance (3 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_69e245b6a3ac81908087599eefe3e365 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f182f54ce88190930530958e2a1830 |
completed | April 29, 2026, 4:03 a.m. |
| PD | Predicate disambiguation | batch_69ef3b974e7c8190b8be11dbb4518693 |
completed | April 27, 2026, 10:33 a.m. |
Created at: April 17, 2026, 3:50 p.m.