Triple
T2877263
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kumbhalgarh Fort |
E56906
|
entity |
| Predicate | hasGate |
P4365
|
FINISHED |
| Object |
Vijay Pol
Vijay Pol is the main entrance gate of Rajasthan’s Kumbhalgarh Fort, known for its imposing architecture and strategic defensive design.
|
E307222
|
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: Vijay Pol | Statement: [Kumbhalgarh Fort, hasGate, Vijay Pol]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vijay Pol Context triple: [Kumbhalgarh Fort, hasGate, Vijay Pol]
-
A.
Vijay Joshi
Vijay Joshi is an Indian economist known for his influential work on macroeconomic policy and development, particularly in the context of the Indian economy.
-
B.
Vijay Vasudevan
Vijay Vasudevan is a computer scientist known for his work in machine learning and systems research, including co-authoring influential papers with Christian Szegedy.
-
C.
Dileep Rao
Dileep Rao is an American actor known for his supporting roles in major films such as Avatar, Drag Me to Hell, and Inception.
-
D.
Kamal Shirwadkar
Kamal Shirwadkar was the wife of renowned Marathi poet and writer Vishnu Vaman Shirwadkar, popularly known as Kusumagraj.
-
E.
Neal Mohan
Neal Mohan is an Indian-American technology executive and digital advertising expert who serves as the CEO of YouTube.
- 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: Vijay Pol Triple: [Kumbhalgarh Fort, hasGate, Vijay Pol]
Generated description
Vijay Pol is the main entrance gate of Rajasthan’s Kumbhalgarh Fort, known for its imposing architecture and strategic defensive design.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Vijay Pol Target entity description: Vijay Pol is the main entrance gate of Rajasthan’s Kumbhalgarh Fort, known for its imposing architecture and strategic defensive design.
-
A.
Vijay Joshi
Vijay Joshi is an Indian economist known for his influential work on macroeconomic policy and development, particularly in the context of the Indian economy.
-
B.
Vijay Vasudevan
Vijay Vasudevan is a computer scientist known for his work in machine learning and systems research, including co-authoring influential papers with Christian Szegedy.
-
C.
Dileep Rao
Dileep Rao is an American actor known for his supporting roles in major films such as Avatar, Drag Me to Hell, and Inception.
-
D.
Kamal Shirwadkar
Kamal Shirwadkar was the wife of renowned Marathi poet and writer Vishnu Vaman Shirwadkar, popularly known as Kusumagraj.
-
E.
Neal Mohan
Neal Mohan is an Indian-American technology executive and digital advertising expert who serves as the CEO of YouTube.
- 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_69ab4a4ced288190ab6d3e062d10f7f6 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abe007329c8190b0bc1851c7307124 |
completed | March 7, 2026, 8:21 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b03158d0ec8190ba9675ece8f62033 |
completed | March 10, 2026, 2:57 p.m. |
| NEDg | Description generation | batch_69b033afe0e08190b6fbf0ec786a71eb |
completed | March 10, 2026, 3:07 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b03437b4d081908c02c01a4dd665b1 |
completed | March 10, 2026, 3:09 p.m. |
Created at: March 6, 2026, 10:03 p.m.