Triple
T23461675
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John E. Hopcroft |
E568994
|
entity |
| Predicate | coauthorWith |
P2389
|
FINISHED |
| Object | Rajeev Motwani |
—
|
NE NERFINISHED |
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: Rajeev Motwani | Statement: [John E. Hopcroft, coauthorWith, Rajeev Motwani]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rajeev Motwani Context triple: [John E. Hopcroft, coauthorWith, Rajeev Motwani]
-
A.
Rajeev Motwani
chosen
Rajeev Motwani was an influential Indian-American computer scientist known for his contributions to theoretical computer science, algorithms, and his mentorship in Silicon Valley.
-
B.
Vijay Vazirani
Vijay Vazirani is an Indian-American theoretical computer scientist known for his influential work in algorithms, computational complexity, and approximation algorithms.
-
C.
Sanjoy Dasgupta
Sanjoy Dasgupta is a computer scientist known for his influential work in machine learning, clustering, and theoretical computer science.
-
D.
Pankaj K. Agarwal
Pankaj K. Agarwal is a computer scientist known for his contributions to computational geometry, algorithms, and data structures.
-
E.
Subhash Suri
Subhash Suri is a computer scientist known for his contributions to algorithms, computational geometry, and network routing.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e2458ebd808190b3298163132cfb0b |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1a69bc200819096ed2baf25cdee4f |
completed | April 29, 2026, 6:35 a.m. |
Created at: April 17, 2026, 5:54 p.m.