Triple
T14383649
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Terry Winograd |
E356669
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Understanding Natural Language |
E434313
|
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: Understanding Natural Language | Statement: [Terry Winograd, notableWork, Understanding Natural Language]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Understanding Natural Language Context triple: [Terry Winograd, notableWork, Understanding Natural Language]
-
A.
NLU
NLU is the IATA airport code for Felipe Ángeles International Airport, a major commercial airport serving the Mexico City metropolitan area.
-
B.
Natural Language framework
The Natural Language framework is an Apple developer framework that provides tools for natural language processing tasks such as tokenization, tagging, language identification, and text classification on Apple platforms.
-
C.
Semantics and Cognition
Semantics and Cognition is a foundational book in cognitive linguistics that explores how meaning in language is structured and represented in the human mind.
-
D.
“A Computer Program for Understanding Natural Language”
chosen
“A Computer Program for Understanding Natural Language” is a landmark 1968 paper by Terry Winograd that presents an early natural language understanding system capable of interpreting and executing commands in a simulated blocks world.
-
E.
Google Natural Language API
Google Natural Language API is a cloud-based service that uses machine learning to analyze and understand text, offering features like sentiment analysis, entity recognition, syntax parsing, and content classification.
- 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_69d827927c988190ad98bb0360981783 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de900d28c88190a37feee4743563de |
completed | April 14, 2026, 7:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd5511c9e4819089dcbf089ca0dc6a |
completed | May 8, 2026, 3:14 a.m. |
Created at: April 10, 2026, 1:16 a.m.