Triple
T7164474
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Prabhas |
E167031
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Varsham
Varsham is a 2004 Telugu romantic action film that significantly boosted actor Prabhas's popularity in the Indian film industry.
|
E645483
|
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: Varsham | Statement: [Prabhas, notableWork, Varsham]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Varsham Context triple: [Prabhas, notableWork, Varsham]
-
A.
Vorokhta
Vorokhta is a Ukrainian mountain resort village in the Carpathians, known as a gateway for hiking and skiing in the Hoverla and Chornohora ranges.
-
B.
Vishkanya
Vishkanya is a 1991 Indian Hindi-language horror film known for its supernatural revenge plot and early appearance of actress Riya Sen.
-
C.
Vyatka
Vyatka was a historic region and town in northeastern European Russia, known as a frontier area that was gradually incorporated into the centralized Russian state.
-
D.
Vyazemsky
Vyazemsky is a small town in Russia’s Far Eastern Federal District, serving as an administrative center within Khabarovsk Krai.
-
E.
Virganskaya
Virganskaya is a Russian surname most notably borne by Irina Virganskaya, the daughter of former Soviet leader Mikhail Gorbachev.
- 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: Varsham Triple: [Prabhas, notableWork, Varsham]
Generated description
Varsham is a 2004 Telugu romantic action film that significantly boosted actor Prabhas's popularity in the Indian film industry.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Varsham Target entity description: Varsham is a 2004 Telugu romantic action film that significantly boosted actor Prabhas's popularity in the Indian film industry.
-
A.
Vorokhta
Vorokhta is a Ukrainian mountain resort village in the Carpathians, known as a gateway for hiking and skiing in the Hoverla and Chornohora ranges.
-
B.
Vishkanya
Vishkanya is a 1991 Indian Hindi-language horror film known for its supernatural revenge plot and early appearance of actress Riya Sen.
-
C.
Vyatka
Vyatka was a historic region and town in northeastern European Russia, known as a frontier area that was gradually incorporated into the centralized Russian state.
-
D.
Vyazemsky
Vyazemsky is a small town in Russia’s Far Eastern Federal District, serving as an administrative center within Khabarovsk Krai.
-
E.
Virganskaya
Virganskaya is a Russian surname most notably borne by Irina Virganskaya, the daughter of former Soviet leader Mikhail Gorbachev.
- 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_69c68888c10c819095e0383020225758 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e83168a08190937ff46797d94f3e |
completed | March 27, 2026, 8:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7adcc145c8190ba65831ed891a225 |
completed | March 28, 2026, 10:30 a.m. |
| NEDg | Description generation | batch_69c7ae9111048190b9d68932b15aeab9 |
completed | March 28, 2026, 10:33 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c7af44076481908d770dd92ed55277 |
completed | March 28, 2026, 10:36 a.m. |
Created at: March 27, 2026, 2:47 p.m.