Triple
T15791304
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pareto principle |
E382867
|
entity |
| Predicate | isExactLaw |
P62237
|
FINISHED |
| Object | false |
—
|
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: false | Statement: [Pareto principle, isExactLaw, false]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isExactLaw Context triple: [Pareto principle, isExactLaw, false]
-
A.
isExactInSI
Indicates that a quantity or value is defined as an exact, non-approximate value within the International System of Units (SI).
-
B.
containsLaw
Indicates that one entity (such as a document, code, or jurisdiction) includes or encompasses a specific law within it.
-
C.
constantExactness
chosen
Indicates that a value or relationship holds with complete, unvarying precision, without any approximation or deviation.
-
D.
isExactlySolvable
Indicates that a problem, model, or equation can be solved completely and exactly (without approximation) using known analytical or algorithmic methods.
-
E.
exactFor
Indicates that one entity corresponds to or matches another entity with complete precision, without any deviation or approximation.
- 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_69d86da16e188190b89af699f1ed0bfe |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e0b4d819c881908bc43a6124a1bb2e |
completed | April 16, 2026, 10:07 a.m. |
| PD | Predicate disambiguation | batch_69e00537bd1c81908d6e832792fd934f |
completed | April 15, 2026, 9:37 p.m. |
Created at: April 10, 2026, 4:48 a.m.