Triple
T25753807
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kajal Aggarwal |
E648535
|
entity |
| Predicate | teluguFilmDebut |
P158444
|
FINISHED |
| Object |
Lakshmi Kalyanam
Lakshmi Kalyanam is a Telugu-language romantic drama film best known for featuring actress Kajal Aggarwal in one of her earliest leading roles.
|
E1692585
|
NE FINISHED |
How this triple was built (3 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: Lakshmi Kalyanam | Statement: [Kajal Aggarwal, teluguFilmDebut, Lakshmi Kalyanam]
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: Lakshmi Kalyanam Triple: [Kajal Aggarwal, teluguFilmDebut, Lakshmi Kalyanam]
Generated description
Lakshmi Kalyanam is a Telugu-language romantic drama film best known for featuring actress Kajal Aggarwal in one of her earliest leading roles.
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: teluguFilmDebut Context triple: [Kajal Aggarwal, teluguFilmDebut, Lakshmi Kalyanam]
-
A.
debutInTeluguCinema
chosen
Indicates the event or relationship in which an entity makes its first appearance or acting role in Telugu-language cinema.
-
B.
filmIndustryDebut
Indicates the event or point in time when an entity first appears or participates in the film industry, such as through a first film role, production, or related professional activity.
-
C.
filmDebut
Indicates the first film in which an entity (typically a person) appeared or participated, marking their initial entry into film work.
-
D.
filmDebutIn
Indicates the first film in which a person appeared or participated, marking their debut in cinema.
-
E.
soundFilmDebutDate
Indicates the date on which an entity first appeared in a sound film.
- F. None of above.
Provenance (6 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_69e7ab314d788190b3abe19e114080e1 |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69f5fd80a93081909fa651bc57d26884 |
completed | May 2, 2026, 1:34 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a10cc1284708190a5bfdcf86fec63e0 |
completed | May 22, 2026, 9:35 p.m. |
| NEDg | Description generation | batch_6a10cc81bb8881909413a1b8924a0fe2 |
completed | May 22, 2026, 9:37 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a10cd0fbcc08190a12ded88d999feab |
completed | May 22, 2026, 9:39 p.m. |
| PD | Predicate disambiguation | batch_69f4938262ac8190b41f922d0407d272 |
completed | May 1, 2026, 11:50 a.m. |
Created at: April 22, 2026, 4:37 a.m.