Triple

T32264843
Position Surface form Disambiguated ID Type / Status
Subject Gianni Chellini E824252 entity
Predicate fictionalUniverse P3758 FINISHED
Object Transporter film series
The Transporter film series is an action franchise centered on a highly skilled professional driver who undertakes dangerous delivery missions under strict rules, often leading to high-speed chases and intense combat.
 E2000024 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: Transporter film series | Statement: [Gianni Chellini, fictionalUniverse, Transporter film series]
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: Transporter film series
Triple: [Gianni Chellini, fictionalUniverse, Transporter film series]
Generated description
The Transporter film series is an action franchise centered on a highly skilled professional driver who undertakes dangerous delivery missions under strict rules, often leading to high-speed chases and intense combat.

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_69f3490e73588190915f282edd105772 completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6bc7d43a481908327b7740435433b completed May 3, 2026, 3:09 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2f46db79e88190bb877979ab0acdd1 completed June 15, 2026, 12:27 a.m.
NEDg Description generation batch_6a2f476da774819083632efe3902e9ec completed June 15, 2026, 12:29 a.m.
NED2 Entity disambiguation (via description) batch_6a2f47f1621c8190ab4af373fdf9eda8 completed June 15, 2026, 12:31 a.m.
Created at: May 1, 2026, 12:42 a.m.