Triple

T26693052
Position Surface form Disambiguated ID Type / Status
Subject Erica Hill E672940 entity
Predicate hasRole P161 FINISHED
Object correspondent for CNN
A correspondent for CNN is a journalist who reports news stories and provides on-the-ground coverage for the Cable News Network.
 E1736924 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: correspondent for CNN | Statement: [Erica Hill, hasRole, correspondent for CNN]
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: correspondent for CNN
Triple: [Erica Hill, hasRole, correspondent for CNN]
Generated description
A correspondent for CNN is a journalist who reports news stories and provides on-the-ground coverage for the Cable News Network.

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_69eecda2066c8190a344218afa5e89c1 completed April 27, 2026, 2:44 a.m.
NER Named-entity recognition batch_69f61779bf6081909720cef2847ccac0 completed May 2, 2026, 3:25 p.m.
NED1 Entity disambiguation (via context triple) batch_6a11fe7e8b5c819084088f71eb72608f completed May 23, 2026, 7:22 p.m.
NEDg Description generation batch_6a11ff162d588190a1f98429d7fe5154 completed May 23, 2026, 7:25 p.m.
NED2 Entity disambiguation (via description) batch_6a11ff99fdbc81909fd5646fb32987a2 completed May 23, 2026, 7:27 p.m.
Created at: April 27, 2026, 3:27 a.m.