Triple
T293193
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | PISA |
E6037
|
entity |
| Predicate | assesses |
P170
|
FINISHED |
| Object | application of knowledge to real-life situations |
—
|
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: application of knowledge to real-life situations | Statement: [PISA, assesses, application of knowledge to real-life situations]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: assesses Context triple: [PISA, assesses, application of knowledge to real-life situations]
-
A.
assessedBy
Indicates that an entity has been evaluated, examined, or judged by another entity (typically an agent or authority).
-
B.
assessmentMethod
Indicates the method or procedure used to evaluate, measure, or judge something.
-
C.
analyzes
chosen
Indicates that one entity systematically examines or evaluates another entity to understand its nature, structure, or components.
-
D.
assumes
Indicates that one entity takes on, accepts, or presumes a role, responsibility, state, or fact regarding another entity or situation.
-
E.
approves
Indicates that one entity formally accepts, authorizes, or agrees to a proposal, action, or decision made by another entity.
- 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_69a2e79114b081909490b3bf5a5dbb51 |
completed | Feb. 28, 2026, 1:03 p.m. |
| NER | Named-entity recognition | batch_69a2ea0dd1dc8190aecd5afdeb2fd74b |
completed | Feb. 28, 2026, 1:13 p.m. |
| PD | Predicate disambiguation | batch_69a2e934b4408190b53a17f57a02df65 |
completed | Feb. 28, 2026, 1:10 p.m. |
Created at: Feb. 28, 2026, 1:06 p.m.