Triple
T23154195
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sturgeon's Law |
E578396
|
entity |
| Predicate | hasApproximateProportion |
P90324
|
FINISHED |
| Object | 90 percent low quality |
—
|
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: 90 percent low quality | Statement: [Sturgeon's Law, hasApproximateProportion, 90 percent low quality]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasApproximateProportion Context triple: [Sturgeon's Law, hasApproximateProportion, 90 percent low quality]
-
A.
hasProportion
Indicates that one entity stands in a specified ratio, fraction, or relative share to another entity or whole.
-
B.
containsApproximately
chosen
Indicates that one entity holds or includes another entity in a quantity or proportion that is close to, but not exactly, a specified amount.
-
C.
holdsApproximatelyFor
Indicates that a condition, relation, or value is valid only to an approximate degree or within a tolerance, rather than holding exactly.
-
D.
representsApproximately
Indicates that one entity serves as an inexact or close-but-not-exact representation or value of another entity.
-
E.
hasApproximationRatio
Indicates that there exists a quantitative bound describing how closely an algorithm’s solution approximates the optimal solution for a given problem.
- 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_69e245fb8de081908f0eba7b5fd75bc4 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f18efbe9a08190bcb6e822b8eab544 |
completed | April 29, 2026, 4:54 a.m. |
| PD | Predicate disambiguation | batch_69ef89ff76808190808ee4ad9dea776b |
completed | April 27, 2026, 4:08 p.m. |
Created at: April 17, 2026, 4:01 p.m.