Triple
T7984000
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lhasa |
E185643
|
entity |
| Predicate | averageAnnualSunshine |
P57952
|
FINISHED |
| Object | over 3000 hours |
—
|
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: over 3000 hours | Statement: [Lhasa, averageAnnualSunshine, over 3000 hours]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: averageAnnualSunshine Context triple: [Lhasa, averageAnnualSunshine, over 3000 hours]
-
A.
averageAnnualSunshineDays
Indicates the typical number of days per year that a location experiences sunshine, averaged over a specified period.
-
B.
sunshineHoursPerYear
chosen
Indicates the total number of hours in a year during which a location receives direct sunshine.
-
C.
averageAnnualPrecipitation
Indicates the typical total amount of precipitation an entity receives over the course of a year, averaged across multiple years.
-
D.
averageAnnualSnowfall
Indicates the typical amount of snow that falls in a given location over the course of a year, averaged across multiple years.
-
E.
averageTemperature
Indicates the typical or mean temperature value associated with an entity over a specified period or context.
- 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_69ca829a2cfc819083d591d58ec04075 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3c2b543c81909b82bc478d579e0b |
completed | March 31, 2026, 3:14 a.m. |
| PD | Predicate disambiguation | batch_69cb048009a08190b4c577208a9f8f76 |
completed | March 30, 2026, 11:17 p.m. |
Created at: March 30, 2026, 5:15 p.m.