Triple
T3006048
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bombay Beach, California |
E81903
|
entity |
| Predicate | hasSummerTemperatureCharacteristic |
P19296
|
FINISHED |
| Object | very hot summers |
—
|
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: very hot summers | Statement: [Bombay Beach, California, hasSummerTemperatureCharacteristic, very hot summers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSummerTemperatureCharacteristic Context triple: [Bombay Beach, California, hasSummerTemperatureCharacteristic, very hot summers]
-
A.
hasTemperature
Indicates that an entity possesses or is characterized by a specific temperature value.
-
B.
typicalTemperature
chosen
Indicates the usual or characteristic temperature associated with an entity under normal conditions.
-
C.
averageTemperature
Indicates the typical or mean temperature value associated with an entity over a specified period or context.
-
D.
operatingTemperature
Indicates the range or specific value of temperature within which an entity is designed or allowed to function properly.
-
E.
hasHeating
Indicates that an entity is equipped with or provides a heating system or heating capability.
- 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_69ad8b1c4de88190a83b7cefaa1f2842 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9a48f0888190bdec150dac623851 |
completed | March 8, 2026, 3:48 p.m. |
| PD | Predicate disambiguation | batch_69ad96180eb08190a524c5f458d41382 |
completed | March 8, 2026, 3:30 p.m. |
Created at: March 8, 2026, 3 p.m.