Triple
T7164939
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brian Kernighan |
E167041
|
entity |
| Predicate | coCreatorOf |
P806
|
FINISHED |
| Object | AMPL modeling language |
E645517
|
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: AMPL modeling language | Statement: [Brian Kernighan, coCreatorOf, AMPL modeling language]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: AMPL modeling language Context triple: [Brian Kernighan, coCreatorOf, AMPL modeling language]
-
A.
AMPL modeling language
chosen
AMPL modeling language is a high-level algebraic language used to describe and solve large-scale mathematical optimization problems.
-
B.
SCIP
SCIP is the ICAO airport code for Mataveri International Airport, the main air gateway to Easter Island in Chile.
-
C.
CVX
CVX is the stock ticker symbol for Chevron Corporation, a major American multinational energy and oil company.
-
D.
BrainScript modeling language
BrainScript modeling language is a domain-specific scripting language used to define and train neural network models within the Microsoft Cognitive Toolkit (CNTK).
-
E.
ADMM
ADMM is a regional forum that brings together the defence ministers of ASEAN member states to discuss and coordinate security and defence cooperation in Southeast Asia.
- 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_69c68888c10c819095e0383020225758 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e832d2548190aacff0de80dbc268 |
completed | March 27, 2026, 8:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7b901cb608190acd25c22b38a1957 |
completed | March 28, 2026, 11:18 a.m. |
Created at: March 27, 2026, 2:47 p.m.