Triple
T1212060
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | University of Delhi |
E26022
|
entity |
| Predicate | viceChancellor |
P142
|
FINISHED |
| Object |
Yogesh Singh
Yogesh Singh is an Indian academic and administrator who serves as the Vice-Chancellor of the University of Delhi.
|
E178955
|
NE FINISHED |
How this triple was built (4 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: Yogesh Singh | Statement: [University of Delhi, viceChancellor, Yogesh Singh]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yogesh Singh Context triple: [University of Delhi, viceChancellor, Yogesh Singh]
-
A.
Suraj Sharma
Suraj Sharma is an Indian actor best known for his breakout performance as the shipwrecked teenager Pi Patel in Ang Lee’s acclaimed film "Life of Pi."
-
B.
Nirvikar Singh
Nirvikar Singh is an economist and academic known for his contributions to economic theory and policy, associated with leading institutions such as the Delhi School of Economics.
-
C.
Yogendra Shukla
Yogendra Shukla was an Indian freedom fighter and revolutionary leader associated with the independence movement against British colonial rule.
-
D.
Vijay Kumar
Vijay Kumar is a prominent roboticist and engineer known for his pioneering work in multi-robot systems and aerial robotics.
-
E.
Sachit Mehra
Sachit Mehra is a Canadian political figure who serves in a top leadership role within the Liberal Party of Canada.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Yogesh Singh Triple: [University of Delhi, viceChancellor, Yogesh Singh]
Generated description
Yogesh Singh is an Indian academic and administrator who serves as the Vice-Chancellor of the University of Delhi.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Yogesh Singh Target entity description: Yogesh Singh is an Indian academic and administrator who serves as the Vice-Chancellor of the University of Delhi.
-
A.
Suraj Sharma
Suraj Sharma is an Indian actor best known for his breakout performance as the shipwrecked teenager Pi Patel in Ang Lee’s acclaimed film "Life of Pi."
-
B.
Nirvikar Singh
Nirvikar Singh is an economist and academic known for his contributions to economic theory and policy, associated with leading institutions such as the Delhi School of Economics.
-
C.
Yogendra Shukla
Yogendra Shukla was an Indian freedom fighter and revolutionary leader associated with the independence movement against British colonial rule.
-
D.
Vijay Kumar
Vijay Kumar is a prominent roboticist and engineer known for his pioneering work in multi-robot systems and aerial robotics.
-
E.
Sachit Mehra
Sachit Mehra is a Canadian political figure who serves in a top leadership role within the Liberal Party of Canada.
- F. None of above. chosen
Provenance (5 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_69a4948331fc8190b531ac9bec71c491 |
completed | March 1, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69a4bde6cb608190b77fc5c47083e4b7 |
completed | March 1, 2026, 10:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad4005cd4c81909cff0ed6529d1695 |
completed | March 8, 2026, 9:23 a.m. |
| NEDg | Description generation | batch_69ad4124554c819080978ca73a2c1404 |
completed | March 8, 2026, 9:28 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ad41968e4c8190b843b97e18ac9968 |
completed | March 8, 2026, 9:29 a.m. |
Created at: March 1, 2026, 7:46 p.m.