Triple

T29615604
Position Surface form Disambiguated ID Type / Status
Subject Sattam Oru Iruttarai E754853 entity
Predicate editedBy P1954 FINISHED
Object P. R. Gowthamraj
P. R. Gowthamraj is a film editor known for his work on Tamil-language movies such as "Sattam Oru Iruttarai."
E1882397 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: P. R. Gowthamraj | Statement: [Sattam Oru Iruttarai, editedBy, P. R. Gowthamraj]
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: P. R. Gowthamraj
Triple: [Sattam Oru Iruttarai, editedBy, P. R. Gowthamraj]
Generated description
P. R. Gowthamraj is a film editor known for his work on Tamil-language movies such as "Sattam Oru Iruttarai."

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_6a26aa613a14819090501139c0c4054c completed June 8, 2026, 11:41 a.m.
NEDg Description generation batch_6a26b557450081909c03ff34c171a007 completed June 8, 2026, 12:28 p.m.
NED2 Entity disambiguation (via description) batch_6a26b934b00c819087c306fb9dbc427d completed June 8, 2026, 12:44 p.m.
Created at: April 28, 2026, 6:31 p.m.