Triple
T575680
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Neal Mohan |
E13756
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Neal Mohan |
E13756
|
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: Neal Mohan | Statement: [Neal Mohan, name, Neal Mohan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Neal Mohan Context triple: [Neal Mohan, name, Neal Mohan]
-
A.
Neal Mohan
chosen
Neal Mohan is an Indian-American technology executive and digital advertising expert who serves as the CEO of YouTube.
-
B.
Narhari Parikh
Narhari Parikh was an Indian freedom fighter, social worker, and close associate of Mahatma Gandhi who played a significant role in early Gandhian movements and rural reform.
-
C.
Pradip Krishen
Pradip Krishen is an Indian filmmaker-turned-environmentalist and naturalist known for his documentaries and influential work on urban ecology and tree mapping in India.
-
D.
Naveen Andrews
Naveen Andrews is a British actor best known for his roles in the television series "Lost" and films such as "The English Patient."
-
E.
Sanjiv Singh
Sanjiv Singh is a robotics researcher and professor known for his work in autonomous systems and field robotics at Carnegie Mellon University.
- 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_69a4933fa4d88190a7949cc83c08c5c1 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49b67395c8190a8046ff7debe9d1f |
completed | March 1, 2026, 8:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a5291f50e481909cf404c6b4050b94 |
completed | March 2, 2026, 6:07 a.m. |
Created at: March 1, 2026, 7:33 p.m.