Triple
T58996
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shannon entropy |
E1168
|
entity |
| Predicate | isNonNegative |
P4230
|
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: [Shannon entropy, isNonNegative, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isNonNegative Context triple: [Shannon entropy, isNonNegative, true]
-
A.
hasOppositeNumberForm
Indicates that one entity is represented by a number form that is the opposite (e.g., additive vs. subtractive, positive vs. negative, or otherwise contrastive) of the number form used to represent the other entity.
-
B.
isSubjectTo
Indicates that one entity is governed, affected, or constrained by the authority, rules, conditions, or influence of another entity.
-
C.
isDistinctFrom
Indicates that two entities are not identical and can be clearly distinguished from one another.
-
D.
isCivilian
Indicates that an entity is a non-military, non-combatant individual in the context of a given situation or system.
-
E.
doesNotHave
Indicates that one entity lacks, is missing, or is not in possession of another entity or attribute.
- 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_69a24a552ef88190a0df287d68c65cba |
completed | Feb. 28, 2026, 1:52 a.m. |
| NER | Named-entity recognition | batch_69a250e401288190ba12322c9c5f07c9 |
completed | Feb. 28, 2026, 2:20 a.m. |
| PD | Predicate disambiguation | batch_69a24e9f40908190a2f4a2111469b733 |
completed | Feb. 28, 2026, 2:10 a.m. |
| PDg | Predicate description generation | batch_69a250e2a80881909e5a653260e6f8e0 |
completed | Feb. 28, 2026, 2:20 a.m. |
Created at: Feb. 28, 2026, 1:55 a.m.