Triple
T27208672
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Filmfare Award for Best Female Debut |
E683940
|
entity |
| Predicate | notableRecipient |
P108
|
FINISHED |
| Object |
Disha Patani
Disha Patani is an Indian actress and model known for her work in Hindi films, rising to fame with roles in movies like "M.S. Dhoni: The Untold Story" and "Baaghi 2."
|
E2038944
|
NE FINISHED |
How this triple was built (2 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: Disha Patani | Statement: [Filmfare Award for Best Female Debut, notableRecipient, Disha Patani]
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: Disha Patani Triple: [Filmfare Award for Best Female Debut, notableRecipient, Disha Patani]
Generated description
Disha Patani is an Indian actress and model known for her work in Hindi films, rising to fame with roles in movies like "M.S. Dhoni: The Untold Story" and "Baaghi 2."
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_69eefad339a08190aeacb2a198f1a39b |
completed | April 27, 2026, 5:57 a.m. |
| NER | Named-entity recognition | batch_69f625e6cd708190aea9dc220df25717 |
completed | May 2, 2026, 4:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a3515ef21dc8190a720da87ed78dd1f |
completed | June 19, 2026, 10:11 a.m. |
| NEDg | Description generation | batch_6a3516b1daa08190a21764ce7125ec69 |
completed | June 19, 2026, 10:15 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a35172e773881908a625df198825ef0 |
completed | June 19, 2026, 10:17 a.m. |
Created at: April 27, 2026, 9:38 a.m.