Triple
T14376876
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tórshavn |
E356497
|
entity |
| Predicate | averageTemperatureInJanuary |
P4814
|
FINISHED |
| Object | around 3 °C |
—
|
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: around 3 °C | Statement: [Tórshavn, averageTemperatureInJanuary, around 3 °C]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: averageTemperatureInJanuary Context triple: [Tórshavn, averageTemperatureInJanuary, around 3 °C]
-
A.
averageHighTemperatureInJanuary
Indicates the typical or mean value of the highest daily temperatures recorded during the month of January for a given location.
-
B.
averageJanuaryLowTemperature
Indicates the typical minimum daily temperature experienced in a location during the month of January.
-
C.
averageTemperature
chosen
Indicates the typical or mean temperature value associated with an entity over a specified period or context.
-
D.
averageWinterLowTemperature
Indicates the typical minimum temperature experienced during the winter season for a given location or period.
-
E.
winterAverageTemperature
Indicates the typical or mean temperature recorded during the winter season for a given entity or location.
- 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_69d8279163a081908aec45c0e3f1e02f |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de900949fc81909be0da1734c46645 |
completed | April 14, 2026, 7:05 p.m. |
| PD | Predicate disambiguation | batch_69de2a9cb3e081909f6b33fdd939bb9e |
completed | April 14, 2026, 11:53 a.m. |
Created at: April 10, 2026, 1:16 a.m.