Triple
T1748759
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Jungle Book (2016 film) |
E38392
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object |
Neel Sethi
Neel Sethi is an American actor best known for playing Mowgli in Disney’s 2016 live-action adaptation of The Jungle Book.
|
E204336
|
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: Neel Sethi | Statement: [The Jungle Book (2016 film), stars, Neel Sethi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Neel Sethi Context triple: [The Jungle Book (2016 film), stars, Neel Sethi]
-
A.
Sachit Mehra
Sachit Mehra is a Canadian political figure who serves in a top leadership role within the Liberal Party of Canada.
-
B.
Deepak Nayyar
Deepak Nayyar is an Indian economist and academic known for his work on development economics and his leadership roles in major universities and international economic institutions.
-
C.
Rajat Monga
Rajat Monga is a computer scientist and engineer best known as a co-creator and early lead of TensorFlow at Google Brain.
-
D.
Naveen Andrews
Naveen Andrews is a British actor best known for his roles in the television series "Lost" and films such as "The English Patient."
-
E.
Kunal Nayyar
Kunal Nayyar is a British-Indian actor best known for playing the socially awkward astrophysicist Rajesh Koothrappali on the hit sitcom "The Big Bang Theory."
- 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: Neel Sethi Triple: [The Jungle Book (2016 film), stars, Neel Sethi]
Generated description
Neel Sethi is an American actor best known for playing Mowgli in Disney’s 2016 live-action adaptation of The Jungle Book.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Neel Sethi Target entity description: Neel Sethi is an American actor best known for playing Mowgli in Disney’s 2016 live-action adaptation of The Jungle Book.
-
A.
Sachit Mehra
Sachit Mehra is a Canadian political figure who serves in a top leadership role within the Liberal Party of Canada.
-
B.
Deepak Nayyar
Deepak Nayyar is an Indian economist and academic known for his work on development economics and his leadership roles in major universities and international economic institutions.
-
C.
Rajat Monga
Rajat Monga is a computer scientist and engineer best known as a co-creator and early lead of TensorFlow at Google Brain.
-
D.
Naveen Andrews
Naveen Andrews is a British actor best known for his roles in the television series "Lost" and films such as "The English Patient."
-
E.
Kunal Nayyar
Kunal Nayyar is a British-Indian actor best known for playing the socially awkward astrophysicist Rajesh Koothrappali on the hit sitcom "The Big Bang Theory."
- 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_69a8862b01a48190ab47209063af82d9 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69aa63ee4d2081909dfd6d3244228c56 |
completed | March 6, 2026, 5:19 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adbf4ba8fc81909b538168a4403330 |
completed | March 8, 2026, 6:26 p.m. |
| NEDg | Description generation | batch_69adc2b4d8a0819080ff41cf73417276 |
completed | March 8, 2026, 6:40 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69adc38732d8819092e0ac76354f08c1 |
completed | March 8, 2026, 6:44 p.m. |
Created at: March 4, 2026, 7:31 p.m.