Triple
T14343971
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Azul Systems |
E355666
|
entity |
| Predicate | hasKeyPerson |
P256
|
FINISHED |
| Object | Shyam Pillalamarri |
E1094438
|
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: Shyam Pillalamarri | Statement: [Azul Systems, hasKeyPerson, Shyam Pillalamarri]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shyam Pillalamarri Context triple: [Azul Systems, hasKeyPerson, Shyam Pillalamarri]
-
A.
Shyam Pillalamarri
chosen
Shyam Pillalamarri is a technology entrepreneur best known as a co-founder of the Java-focused software company Azul Systems.
-
B.
Srinu Vaitla
Srinu Vaitla is an Indian film director best known for his work in Telugu cinema, particularly for directing successful commercial comedies and action entertainers.
-
C.
Srinivas Mohan
Srinivas Mohan is an acclaimed Indian visual effects supervisor known for his pioneering VFX work in major South Indian films.
-
D.
Rishikesha T. Krishnan
Rishikesha T. Krishnan is an Indian management scholar and academic leader known for his work on innovation and strategy, and for serving as director of leading Indian Institutes of Management.
-
E.
Madhukar Korupolu
Madhukar Korupolu is a computer scientist known for his contributions to large-scale cluster management and scheduling systems at Google, including work on the Borg cluster management system.
- 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_69d82790a7e08190877e2d349b2e8d8e |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de8e89ed9c8190acdb647ee618e919 |
completed | April 14, 2026, 6:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd4c4145c081909832e2334a064fb0 |
completed | May 8, 2026, 2:36 a.m. |
Created at: April 10, 2026, 1:14 a.m.