Triple
T21195238
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mulk Raj Anand |
E522309
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Mulk Raj
Mulk Raj was a prominent Indian novelist and one of the pioneers of modern Indian English literature, known for his socially conscious and humanist themes.
|
E1472984
|
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: Mulk Raj | Statement: [Mulk Raj Anand, givenName, Mulk Raj]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mulk Raj Context triple: [Mulk Raj Anand, givenName, Mulk Raj]
-
A.
Jagatraj
Jagatraj was a prince of the Bundela dynasty, known as a son of the famed Bundelkhand ruler Maharaja Chhatrasal.
-
B.
Brij Mohan
Brij Mohan is the given name of B. M. Kaul, an individual identifiable by the initials B.M. Kaul.
-
C.
Pran Thapar
Pran Thapar was a senior Indian Army officer who served as the Chief of Army Staff in the early 1960s, including during the Sino-Indian War of 1962.
-
D.
Chander Mohan
Chander Mohan is an Indian politician from Haryana, known as the son of former Chief Minister Bhajan Lal and for serving as Deputy Chief Minister of the state.
-
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: Mulk Raj Triple: [Mulk Raj Anand, givenName, Mulk Raj]
Generated description
Mulk Raj was a prominent Indian novelist and one of the pioneers of modern Indian English literature, known for his socially conscious and humanist themes.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mulk Raj Target entity description: Mulk Raj was a prominent Indian novelist and one of the pioneers of modern Indian English literature, known for his socially conscious and humanist themes.
-
A.
Jagatraj
Jagatraj was a prince of the Bundela dynasty, known as a son of the famed Bundelkhand ruler Maharaja Chhatrasal.
-
B.
Brij Mohan
Brij Mohan is the given name of B. M. Kaul, an individual identifiable by the initials B.M. Kaul.
-
C.
Pran Thapar
Pran Thapar was a senior Indian Army officer who served as the Chief of Army Staff in the early 1960s, including during the Sino-Indian War of 1962.
-
D.
Chander Mohan
Chander Mohan is an Indian politician from Haryana, known as the son of former Chief Minister Bhajan Lal and for serving as Deputy Chief Minister of the state.
-
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
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_69e0b51061388190aa03f19700d3ef04 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e7333aa0fc81909b17eb6a26f389ec |
completed | April 21, 2026, 8:20 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0986ddf7c4819082c3fc3c7d47993a |
completed | May 17, 2026, 9:14 a.m. |
| NEDg | Description generation | batch_6a09883271c08190aaf046130c9b2698 |
completed | May 17, 2026, 9:19 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0988a7c9b081909688dba71dbce654 |
completed | May 17, 2026, 9:21 a.m. |
Created at: April 16, 2026, 3:08 p.m.