Triple
T168934
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tampa, Florida |
E3075
|
entity |
| Predicate | averageHighTemperatureInJanuary |
P5274
|
FINISHED |
| Object | around 70 °F |
—
|
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 70 °F | Statement: [Tampa, Florida, averageHighTemperatureInJanuary, around 70 °F]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: averageHighTemperatureInJanuary Context triple: [Tampa, Florida, averageHighTemperatureInJanuary, around 70 °F]
-
A.
averageTemperature
Indicates the typical or mean temperature value associated with an entity over a specified period or context.
-
B.
hasAverageSpringTemperature
Indicates that an entity is associated with a specific average temperature value measured over the spring season.
-
C.
hasTemperature
Indicates that an entity possesses or is characterized by a specific temperature value.
-
D.
snowfallRecord
Indicates that a specific amount of snow has been measured or documented for a particular place and time.
-
E.
averageAnnualPrecipitation
Indicates the typical total amount of precipitation an entity receives over the course of a year, averaged across multiple years.
- 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_69a2524ce1e48190ab066bf72859f474 |
completed | Feb. 28, 2026, 2:26 a.m. |
| NER | Named-entity recognition | batch_69a258b6f4f88190b1264bbbeb19a29e |
completed | Feb. 28, 2026, 2:53 a.m. |
| PD | Predicate disambiguation | batch_69a25665f5b8819096ca3e084faf976e |
completed | Feb. 28, 2026, 2:43 a.m. |
| PDg | Predicate description generation | batch_69a25710bdfc81909b6697159104cf53 |
completed | Feb. 28, 2026, 2:46 a.m. |
Created at: Feb. 28, 2026, 2:34 a.m.