Triple

T29615599
Position Surface form Disambiguated ID Type / Status
Subject Sattam Oru Iruttarai E754853 entity
Predicate starring P1507 FINISHED
Object Poornima Jayaram
Poornima Jayaram is an Indian actress known for her work in Tamil and Malayalam cinema during the 1980s.
E1960243 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: Poornima Jayaram | Statement: [Sattam Oru Iruttarai, starring, Poornima Jayaram]
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: Poornima Jayaram
Triple: [Sattam Oru Iruttarai, starring, Poornima Jayaram]
Generated description
Poornima Jayaram is an Indian actress known for her work in Tamil and Malayalam cinema during the 1980s.

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_69f0ef85f62081909842b59fdf8717e1 completed April 28, 2026, 5:33 p.m.
NER Named-entity recognition batch_69f66e20cefc8190aacd49631e0c5274 completed May 2, 2026, 9:35 p.m.
NED1 Entity disambiguation (via context triple) batch_6a2ad20d5448819097632965213c9fe2 completed June 11, 2026, 3:19 p.m.
NEDg Description generation batch_6a2ad3d7a5f08190839786c75b37b39b completed June 11, 2026, 3:27 p.m.
NED2 Entity disambiguation (via description) batch_6a2ae074e4d48190954cc37d2df4771d completed June 11, 2026, 4:21 p.m.
Created at: April 28, 2026, 6:31 p.m.