Triple
T529702
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ginzburg–Landau theory of superconductivity |
E10997
|
entity |
| Predicate | validNear |
P9768
|
FINISHED |
| Object | critical temperature |
—
|
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: critical temperature | Statement: [Ginzburg–Landau theory of superconductivity, validNear, critical temperature]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: validNear Context triple: [Ginzburg–Landau theory of superconductivity, validNear, critical temperature]
-
A.
near
Indicates that one entity is located at a short distance from another entity in space or position.
-
B.
occursNear
Indicates that one event or entity takes place or exists in close spatial proximity to another.
-
C.
validIn
chosen
Indicates that a given entity, statement, or condition is applicable, correct, or legally/semantically acceptable within a specified context, scope, or domain.
-
D.
hasNearbySquare
Indicates that one entity has at least one square-shaped entity located close to it in space.
-
E.
hasNearbyFacility
Indicates that one entity is located close to or in the vicinity of a particular facility.
- 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_69a2e84b16c4819088d284c47c3a7968 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f1d4984c8190ac372171b16bb5e4 |
completed | Feb. 28, 2026, 1:47 p.m. |
| PD | Predicate disambiguation | batch_69a2f01ac3ec8190a94a05955532c7fa |
completed | Feb. 28, 2026, 1:39 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.