Triple
T22074372
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dilip Vengsarkar |
E545486
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Dilip
Dilip is a common Indian male given name, notably borne by former cricketer Dilip Vengsarkar.
|
E30476
|
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: Dilip | Statement: [Dilip Vengsarkar, givenName, Dilip]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dilip Context triple: [Dilip Vengsarkar, givenName, Dilip]
-
A.
Sumant
Sumant was a key administrative office in the Maratha Empire responsible for handling foreign affairs and diplomatic correspondence.
-
B.
Amitabh Shukla
Amitabh Shukla is an Indian film editor known for his work on notable Bollywood films, including the historical drama "Mangal Pandey: The Rising."
-
C.
Upendra
Upendra is a name and epithet of the Hindu god Vishnu, particularly associated with his dwarf incarnation Vamana.
-
D.
Dilip Hiro
Dilip Hiro is a British-based Indian author, journalist, and commentator known for his extensive writings on Middle Eastern politics, South Asia, and global geopolitics.
-
E.
Huggy Rao
Huggy Rao is a Stanford Graduate School of Business professor and organizational scholar known for his work on scaling excellence, organizational change, and market dynamics.
- 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: Dilip Triple: [Dilip Vengsarkar, givenName, Dilip]
Generated description
Dilip is a common Indian male given name, notably borne by former cricketer Dilip Vengsarkar.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dilip Target entity description: Dilip is a common Indian male given name, notably borne by former cricketer Dilip Vengsarkar.
-
A.
Sumant
Sumant was a key administrative office in the Maratha Empire responsible for handling foreign affairs and diplomatic correspondence.
-
B.
Amitabh Shukla
Amitabh Shukla is an Indian film editor known for his work on notable Bollywood films, including the historical drama "Mangal Pandey: The Rising."
-
C.
Upendra
Upendra is a name and epithet of the Hindu god Vishnu, particularly associated with his dwarf incarnation Vamana.
-
D.
Dilip Hiro
chosen
Dilip Hiro is a British-based Indian author, journalist, and commentator known for his extensive writings on Middle Eastern politics, South Asia, and global geopolitics.
-
E.
Huggy Rao
Huggy Rao is a Stanford Graduate School of Business professor and organizational scholar known for his work on scaling excellence, organizational change, and market dynamics.
- F. None of above.
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_69e11e344dfc81909b1d88a7221329c7 |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f1288affb081908b64742f7bf467fa |
completed | April 28, 2026, 9:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0a80b5af408190baa2d74900fc7444 |
completed | May 18, 2026, 3 a.m. |
| NEDg | Description generation | batch_6a0a819c890481908303786efeef595c |
completed | May 18, 2026, 3:03 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0a826075988190a7148ded16f779c0 |
completed | May 18, 2026, 3:07 a.m. |
Created at: April 16, 2026, 8:28 p.m.