Triple
T2682990
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gross–Pitaevskii equation |
E57416
|
entity |
| Predicate | approximationValidity |
P32406
|
FINISHED |
| Object | low temperature limit |
—
|
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: low temperature limit | Statement: [Gross–Pitaevskii equation, approximationValidity, low temperature limit]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approximationValidity Context triple: [Gross–Pitaevskii equation, approximationValidity, low temperature limit]
-
A.
approximationType
Indicates the specific method or scheme used to approximate a value, function, or relationship in a given context.
-
B.
approximates
Indicates that one entity is close to, but not exactly equal to, the value, form, or behavior of another entity.
-
C.
hasValidity
Indicates that something possesses a period or condition during which it is considered legally, logically, or functionally acceptable or in force.
-
D.
validityType
chosen
Indicates the specific kind or category of validity that characterizes how or under what conditions something is considered valid.
-
E.
hasApproximateValue
Indicates that one entity’s value is close to, but not exactly equal to, the value of another entity within an acceptable margin of error.
- 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_69ab4a5028388190a36f3baf1588309e |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd9d602848190b638e417e710a555 |
completed | March 7, 2026, 7:55 a.m. |
| PD | Predicate disambiguation | batch_69abd81c9b4c81908e5e0da6ac5f828b |
completed | March 7, 2026, 7:47 a.m. |
Created at: March 6, 2026, 9:54 p.m.