Triple
T14721126
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Probably Approximately Correct |
E345815
|
entity |
| Predicate | title |
P38
|
FINISHED |
| Object | Probably Approximately Correct |
E345811
|
NE 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: Probably Approximately Correct | Statement: [Probably Approximately Correct, title, Probably Approximately Correct]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Probably Approximately Correct Context triple: [Probably Approximately Correct, title, Probably Approximately Correct]
-
A.
Probably Approximately Correct learning (PAC learning)
chosen
Probably Approximately Correct (PAC) learning is a foundational framework in computational learning theory that formalizes what it means for an algorithm to efficiently learn a concept from examples with high probability and small error.
-
B.
“Probably Approximately Correct” (book)
“Probably Approximately Correct” is a 2013 book by computer scientist Leslie Valiant that explores how ideas from computational learning theory can explain intelligence, evolution, and the way we understand the world.
-
C.
Charikar
Charikar is a city in northern Afghanistan that serves as the capital of Parwan Province and a key hub on the route between Kabul and the northern regions.
-
D.
Computational Learning Theory
Computational Learning Theory is a branch of computer science and mathematics that studies the design and analysis of algorithms that can learn patterns or functions from data, often using formal models of learning and complexity.
-
E.
Yao’s minimax principle
Yao’s minimax principle is a fundamental result in computational complexity and randomized algorithms that relates the performance of randomized algorithms to the performance of deterministic algorithms against a worst-case input distribution.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d822e5911c8190ba589f957dbd9ba7 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dec25d56fc8190871873ca55d49272 |
completed | April 14, 2026, 10:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fdf0957bb081908f1f382f3be8ec20 |
completed | May 8, 2026, 2:17 p.m. |
Created at: April 10, 2026, 1:29 a.m.