Triple
T6734837
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Narsinghpur |
E153726
|
entity |
| Predicate | winterTemperatureRangeApprox |
P71962
|
FINISHED |
| Object | around 8–10 °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 8–10 °C | Statement: [Narsinghpur, winterTemperatureRangeApprox, around 8–10 °C]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: winterTemperatureRangeApprox Context triple: [Narsinghpur, winterTemperatureRangeApprox, around 8–10 °C]
-
A.
winterTemperatureRange_C
chosen
Indicates the range of temperatures, in degrees Celsius, typically experienced during the winter season for the subject.
-
B.
averageWinterLowTemperature
Indicates the typical minimum temperature experienced during the winter season for a given location or period.
-
C.
winterStatus
Indicates the condition, phase, or circumstances associated with the winter season for a given entity or context.
-
D.
averageWinterHighF
Indicates the typical or mean high temperature, measured in degrees Fahrenheit, during the winter season for the referenced entity.
-
E.
winterFrequency
Indicates how often the related event, condition, or phenomenon occurs during the winter season.
- 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_69c6880bdd68819097de8b6099992682 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d16ecbe08190b019d547f631a725 |
completed | March 27, 2026, 6:50 p.m. |
| PD | Predicate disambiguation | batch_69c6d09067a0819087ed6c820f4699f8 |
completed | March 27, 2026, 6:46 p.m. |
Created at: March 27, 2026, 2:09 p.m.