Triple
T4094720
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | poverty of the stimulus argument |
E87785
|
entity |
| Predicate | relatesTo |
P37
|
FINISHED |
| Object | Gold’s theorem in language learnability |
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: Gold’s theorem in language learnability | Statement: [poverty of the stimulus argument, relatesTo, Gold’s theorem in language learnability]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gold’s theorem in language learnability Context triple: [poverty of the stimulus argument, relatesTo, Gold’s theorem in language learnability]
-
A.
Mathematical Structures of Language
Mathematical Structures of Language is a foundational work in mathematical linguistics that applies formal and algebraic methods to analyze the structure of natural languages.
-
B.
General and Rational Grammar
General and Rational Grammar is a 17th-century French linguistic treatise from the Port-Royal school that seeks to explain the universal, rational principles underlying all human languages.
-
C.
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.
-
D.
Lectures on Government and Binding
Lectures on Government and Binding is a foundational book by Noam Chomsky that systematically presents the Government and Binding framework in generative syntax.
-
E.
Principles and Parameters Theory
Principles and Parameters Theory is a framework in generative linguistics that explains how universal grammatical principles and language-specific parameter settings account for the diversity and acquisition of human languages.
- 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_69aed94425148190be337845d56fac22 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefcdc1ce08190922f55f812b0fda3 |
completed | March 9, 2026, 5:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b56b6f8bb081908aa2d126fe3c9502 |
completed | March 14, 2026, 2:06 p.m. |
Created at: March 9, 2026, 3:40 p.m.