Triple

T32543569
Position Surface form Disambiguated ID Type / Status
Subject Sara Stanley E831780 entity
Predicate relativeOf P367 FINISHED
Object Felicity King
Felicity King is a spirited and sometimes stubborn young girl from the Canadian television series "Road to Avonlea," known for her strong will and growth within the close-knit King family.
E1516685 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: Felicity King | Statement: [Sara Stanley, relativeOf, Felicity King]
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: Felicity King
Triple: [Sara Stanley, relativeOf, Felicity King]
Generated description
Felicity King is a spirited and sometimes stubborn young girl from the Canadian television series "Road to Avonlea," known for her strong will and growth within the close-knit King family.

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_69f34925fd08819084cfe4ec566cb704 completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6c5b7e46081909975b05f7298cc0e completed May 3, 2026, 3:49 a.m.
NED1 Entity disambiguation (via context triple) batch_6a34707f00a08190a694c5bc8ea45ca0 completed June 18, 2026, 10:26 p.m.
NEDg Description generation batch_6a34711d59148190bef0f5cc0177a0ca completed June 18, 2026, 10:28 p.m.
NED2 Entity disambiguation (via description) batch_6a3471ee70d881909bd9668f4d0eb45b completed June 18, 2026, 10:32 p.m.
Created at: May 1, 2026, 1:02 a.m.