Triple
T3673967
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lara |
E77944
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Lara Dutta
Lara Dutta is an Indian actress, model, and former Miss Universe (2000) known for her work in Bollywood films.
|
E377457
|
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: Lara Dutta | Statement: [Lara, hasNotableBearer, Lara Dutta]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lara Dutta Context triple: [Lara, hasNotableBearer, Lara Dutta]
-
A.
Riya Sen
Riya Sen is an Indian actress and model known for her work in Hindi, Bengali, and other regional films, as well as for her prominent presence in Indian popular culture and fashion.
-
B.
Sonam Kapoor
Sonam Kapoor is a prominent Indian actress and fashion icon known for her work in Hindi cinema and her influential presence in the fashion industry.
-
C.
Kareen
Kareen is a feminine given name, typically considered a variant spelling of names like Carine or Karen.
-
D.
Nikita Gill
Nikita Gill is a contemporary British-Indian poet and writer known for her emotionally resonant, feminist poetry and modern retellings of myths and fairy tales.
-
E.
Kareena Kapoor Khan
Kareena Kapoor Khan is a prominent Indian film actress known for her versatile roles in Bollywood and her influential presence in contemporary Hindi cinema.
- 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: Lara Dutta Triple: [Lara, hasNotableBearer, Lara Dutta]
Generated description
Lara Dutta is an Indian actress, model, and former Miss Universe (2000) known for her work in Bollywood films.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lara Dutta Target entity description: Lara Dutta is an Indian actress, model, and former Miss Universe (2000) known for her work in Bollywood films.
-
A.
Riya Sen
Riya Sen is an Indian actress and model known for her work in Hindi, Bengali, and other regional films, as well as for her prominent presence in Indian popular culture and fashion.
-
B.
Sonam Kapoor
Sonam Kapoor is a prominent Indian actress and fashion icon known for her work in Hindi cinema and her influential presence in the fashion industry.
-
C.
Kareen
Kareen is a feminine given name, typically considered a variant spelling of names like Carine or Karen.
-
D.
Nikita Gill
Nikita Gill is a contemporary British-Indian poet and writer known for her emotionally resonant, feminist poetry and modern retellings of myths and fairy tales.
-
E.
Kareena Kapoor Khan
Kareena Kapoor Khan is a prominent Indian film actress known for her versatile roles in Bollywood and her influential presence in contemporary Hindi cinema.
- 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_69ad85e083008190b2e1b7085fe500bd |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc45fbd1c819099023791452f1beb |
completed | March 8, 2026, 6:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b48854a2308190ba6a9fc39929b35c |
completed | March 13, 2026, 9:57 p.m. |
| NEDg | Description generation | batch_69b48b48c5f48190b9db5f3feca08e0e |
completed | March 13, 2026, 10:10 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b4af0410e8819091cb1259d06d60cc |
completed | March 14, 2026, 12:42 a.m. |
Created at: March 8, 2026, 3:25 p.m.