Triple
T115539
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Indian Rebellion of 1857 |
E2329
|
entity |
| Predicate | commander |
P1061
|
FINISHED |
| Object |
Nana Sahib
Nana Sahib was a prominent Indian aristocrat and leader who played a key role in directing rebel forces against British rule during the Indian Rebellion of 1857.
|
E13286
|
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: Nana Sahib | Statement: [Indian Rebellion of 1857, commander, Nana Sahib]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nana Sahib Context triple: [Indian Rebellion of 1857, commander, Nana Sahib]
-
A.
Rani Lakshmibai
Rani Lakshmibai was the queen of Jhansi and a legendary Indian freedom fighter renowned for her bravery and leadership against British colonial rule during the mid-19th century.
-
B.
Rita
Rita is a feminine given name used in various cultures, often as a short form of names like Margarita.
-
C.
Barbara
Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
-
D.
Amara Namani
Amara Namani is a young, resourceful Jaeger pilot and central protagonist in the science fiction film "Pacific Rim: Uprising."
-
E.
Swayam
Swayam is an Indian government-backed online learning platform that provides free Massive Open Online Courses (MOOCs) from schools, colleges, and universities across the country.
- 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: Nana Sahib Triple: [Indian Rebellion of 1857, commander, Nana Sahib]
Generated description
Nana Sahib was a prominent Indian aristocrat and leader who played a key role in directing rebel forces against British rule during the Indian Rebellion of 1857.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Nana Sahib Target entity description: Nana Sahib was a prominent Indian aristocrat and leader who played a key role in directing rebel forces against British rule during the Indian Rebellion of 1857.
-
A.
Rani Lakshmibai
Rani Lakshmibai was the queen of Jhansi and a legendary Indian freedom fighter renowned for her bravery and leadership against British colonial rule during the mid-19th century.
-
B.
Rita
Rita is a feminine given name used in various cultures, often as a short form of names like Margarita.
-
C.
Barbara
Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
-
D.
Amara Namani
Amara Namani is a young, resourceful Jaeger pilot and central protagonist in the science fiction film "Pacific Rim: Uprising."
-
E.
Swayam
Swayam is an Indian government-backed online learning platform that provides free Massive Open Online Courses (MOOCs) from schools, colleges, and universities across the country.
- 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_69a2506c5428819085c28a8884790e29 |
completed | Feb. 28, 2026, 2:18 a.m. |
| NER | Named-entity recognition | batch_69a256f1278881909dc9c17113d2cca2 |
completed | Feb. 28, 2026, 2:46 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a29820a4948190aea1d566cc3738ac |
completed | Feb. 28, 2026, 7:24 a.m. |
| NEDg | Description generation | batch_69a298822e7c8190b37a31a9cc19bc54 |
completed | Feb. 28, 2026, 7:25 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a298f38e4881908210642539f15378 |
completed | Feb. 28, 2026, 7:27 a.m. |
Created at: Feb. 28, 2026, 2:24 a.m.