Triple
T15666976
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kishor S. Trivedi |
E377210
|
entity |
| Predicate | hasResearchInterest |
P934
|
FINISHED |
| Object | Markov chains |
E48274
|
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: Markov chains | Statement: [Kishor S. Trivedi, hasResearchInterest, Markov chains]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Markov chains Context triple: [Kishor S. Trivedi, hasResearchInterest, Markov chains]
-
A.
Markov processes
chosen
Markov processes are stochastic processes in which the future evolution depends only on the present state and not on the past history.
-
B.
Markov
Markov is a Russian surname most famously associated with mathematician Andrey Markov, known for his pioneering work on stochastic processes and Markov chains.
-
C.
Stochastic Processes
"Stochastic Processes" is a foundational textbook by Emanuel Parzen that rigorously introduces the theory and applications of random processes in probability and statistics.
-
D.
Stochastic Processes
Stochastic Processes is a foundational 1953 monograph by Joseph L. Doob that rigorously develops the theory of stochastic processes and modern probability using measure-theoretic methods.
-
E.
Hidden Markov Model
A Hidden Markov Model is a statistical model that represents systems with unobserved (hidden) states generating observable outputs, widely used for sequence analysis tasks such as speech recognition, bioinformatics, and natural language processing.
- 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_69d85cd2e28481909d4e975bee20872f |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04f1151548190a14607e762686cb1 |
completed | April 16, 2026, 2:53 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff67a22888819092ea521bbad7bcb7 |
completed | May 9, 2026, 4:58 p.m. |
Created at: April 10, 2026, 4:16 a.m.