Triple
T210628
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bekenstein–Hawking entropy |
E4708
|
entity |
| Predicate | relatesGeometryTo |
P9153
|
FINISHED |
| Object | thermodynamic entropy |
—
|
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: thermodynamic entropy | Statement: [Bekenstein–Hawking entropy, relatesGeometryTo, thermodynamic entropy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relatesGeometryTo Context triple: [Bekenstein–Hawking entropy, relatesGeometryTo, thermodynamic entropy]
-
A.
relatedTo
Indicates a general, non-specific relationship or association exists between two entities.
-
B.
hasGeodesics
Indicates that the subject possesses or is characterized by geodesic paths, typically representing shortest or straightest possible routes within a given space or geometry.
-
C.
relatedField
Indicates that one field, topic, or area of study is connected or relevant to another in subject matter or application.
-
D.
connectsTo
Indicates a relationship where one entity is linked or joined to another, allowing interaction, communication, or transfer between them.
-
E.
hasRelativeLocation
Indicates that one entity is positioned in space in relation to another entity’s location.
- 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_69a2575cb1dc8190a01ad332426dc339 |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25d35aa288190966b6e15af1525cb |
completed | Feb. 28, 2026, 3:12 a.m. |
| PD | Predicate disambiguation | batch_69a25b4f71b88190866c8262922ae204 |
completed | Feb. 28, 2026, 3:04 a.m. |
| PDg | Predicate description generation | batch_69a25d3463648190ac716d7475378536 |
completed | Feb. 28, 2026, 3:12 a.m. |
Created at: Feb. 28, 2026, 2:52 a.m.