Triple
T2173765
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | London penetration depth |
E48480
|
entity |
| Predicate | temperatureDependent |
P37402
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [London penetration depth, temperatureDependent, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: temperatureDependent Context triple: [London penetration depth, temperatureDependent, true]
-
A.
temperatureProportionalTo
Indicates that the temperature of one entity changes in direct proportion to the temperature of another entity.
-
B.
hasTemperature
Indicates that an entity possesses or is characterized by a specific temperature value.
-
C.
typicalTemperature
Indicates the usual or characteristic temperature associated with an entity under normal conditions.
-
D.
temperatureControlMethod
Indicates the method or mechanism used to regulate or maintain a desired temperature.
-
E.
operatingTemperature
Indicates the range or specific value of temperature within which an entity is designed or allowed to function properly.
- 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_69a88aa3faa48190995b233af6525815 |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc1559ff481908efe3f214b2570dc |
completed | March 7, 2026, 6:10 a.m. |
| PD | Predicate disambiguation | batch_69abbd9efc1c81909a65044a1ffc9038 |
completed | March 7, 2026, 5:54 a.m. |
| PDg | Predicate description generation | batch_69abc153b22481908115e5f582c93f12 |
completed | March 7, 2026, 6:10 a.m. |
Created at: March 4, 2026, 7:45 p.m.