Triple

T25121872
Position Surface form Disambiguated ID Type / Status
Subject Trata de blancas E629286 entity
Predicate translatedTitle P6688 FINISHED
Object White Slave Traffic
White Slave Traffic is a 1913 Argentine silent film that dramatizes the exploitation and trafficking of women, reflecting early cinematic engagement with social issues.
 E1666482 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: White Slave Traffic | Statement: [Trata de blancas, translatedTitle, White Slave Traffic]
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: White Slave Traffic
Triple: [Trata de blancas, translatedTitle, White Slave Traffic]
Generated description
White Slave Traffic is a 1913 Argentine silent film that dramatizes the exploitation and trafficking of women, reflecting early cinematic engagement with social issues.

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_69e2ff3288048190bd82c3b7f7bd0e62 completed April 18, 2026, 3:49 a.m.
NER Named-entity recognition batch_69f465cd44188190827d138b292a411a completed May 1, 2026, 8:35 a.m.
NED1 Entity disambiguation (via context triple) batch_6a105cf6a4c08190a21b63f6537a2cdf completed May 22, 2026, 1:41 p.m.
NEDg Description generation batch_6a105dd12cd08190b382c57952107fa6 completed May 22, 2026, 1:44 p.m.
NED2 Entity disambiguation (via description) batch_6a105edf54888190a3b77f63eb867749 completed May 22, 2026, 1:49 p.m.
Created at: April 18, 2026, 6:28 a.m.