Triple
T11102727
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Karisma Kapoor |
E262551
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Karisma
Karisma is the first name of Karisma Kapoor, a prominent Indian film actress known for her work in Hindi cinema since the 1990s.
|
E904741
|
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: Karisma | Statement: [Karisma Kapoor, givenName, Karisma]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Karisma Context triple: [Karisma Kapoor, givenName, Karisma]
-
A.
Sanam
Sanam is an archaeological site in Sudan’s Napatan region, known for its ancient Kushite remains and its inclusion in the UNESCO-listed Gebel Barkal and associated sites.
-
B.
Aradhana
Aradhana is a landmark 1969 Hindi romantic drama film, celebrated for its music and performances, that significantly boosted the stardom of its lead actors.
-
C.
Kesari
Kesari is a figure in Hindu mythology known as the vanara chief and father of the deity Hanuman.
-
D.
Badal
Badal is a Barcelona Metro station that serves the area near Camp Nou stadium in Barcelona, Spain.
-
E.
Kaalpurush
Kaalpurush is an acclaimed Bengali film by director Buddhadeb Dasgupta that explores memory, time, and human relationships through a poetic, surreal narrative.
- 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: Karisma Triple: [Karisma Kapoor, givenName, Karisma]
Generated description
Karisma is the first name of Karisma Kapoor, a prominent Indian film actress known for her work in Hindi cinema since the 1990s.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Karisma Target entity description: Karisma is the first name of Karisma Kapoor, a prominent Indian film actress known for her work in Hindi cinema since the 1990s.
-
A.
Sanam
Sanam is an archaeological site in Sudan’s Napatan region, known for its ancient Kushite remains and its inclusion in the UNESCO-listed Gebel Barkal and associated sites.
-
B.
Aradhana
Aradhana is a landmark 1969 Hindi romantic drama film, celebrated for its music and performances, that significantly boosted the stardom of its lead actors.
-
C.
Kesari
Kesari is a figure in Hindu mythology known as the vanara chief and father of the deity Hanuman.
-
D.
Badal
Badal is a Barcelona Metro station that serves the area near Camp Nou stadium in Barcelona, Spain.
-
E.
Kaalpurush
Kaalpurush is an acclaimed Bengali film by director Buddhadeb Dasgupta that explores memory, time, and human relationships through a poetic, surreal narrative.
- 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_69d6aa9a40d88190a373e2c7e48285db |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d79a2c30a481908c45020c37caebe4 |
completed | April 9, 2026, 12:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e3e7f9b46881909761ed448fa5ce6e |
completed | April 18, 2026, 8:22 p.m. |
| NEDg | Description generation | batch_69e3f2cc9b7c8190bb5fd89f239917cf |
completed | April 18, 2026, 9:08 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69e3f4a37b6c81908ca63270d82579ae |
completed | April 18, 2026, 9:16 p.m. |
Created at: April 8, 2026, 9:27 p.m.