Triple
T3304426
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | falsificationism |
E69412
|
entity |
| Predicate | criterionFor |
P36515
|
FINISHED |
| Object | demarcation between science and non-science |
—
|
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: demarcation between science and non-science | Statement: [falsificationism, criterionFor, demarcation between science and non-science]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: criterionFor Context triple: [falsificationism, criterionFor, demarcation between science and non-science]
-
A.
evaluationCriteriaInclude
chosen
Indicates that certain criteria are part of, or explicitly included in, the set of standards used to evaluate something.
-
B.
selectionCriteria
Indicates the conditions or rules used to choose certain entities from a larger set.
-
C.
hasSubcriterion
Indicates that a criterion includes another, more specific criterion as a subordinate part of its evaluation structure.
-
D.
notableCriterion
Indicates that something is distinguished or recognized based on a particular standard, measure, or qualifying condition.
-
E.
primaryCriterion
Indicates that one factor is designated as the main or most important basis for a decision, judgment, or selection among alternatives.
- 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_69ad859f218081909458d2cebbf57565 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb0c8179081908a2595d1fdb7560a |
completed | March 8, 2026, 5:24 p.m. |
| PD | Predicate disambiguation | batch_69ada42625308190be257f16a623a410 |
completed | March 8, 2026, 4:30 p.m. |
Created at: March 8, 2026, 3:11 p.m.