Triple

T33140980
Position Surface form Disambiguated ID Type / Status
Subject Shawn Ashmore E848146 entity
Predicate spouse P13 FINISHED
Object Dana Wasdin
Dana Wasdin is the wife of Canadian actor Shawn Ashmore, known for his roles in the X-Men film series and various television shows.
E2037717 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: Dana Wasdin | Statement: [Shawn Ashmore, spouse, Dana Wasdin]
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: Dana Wasdin
Triple: [Shawn Ashmore, spouse, Dana Wasdin]
Generated description
Dana Wasdin is the wife of Canadian actor Shawn Ashmore, known for his roles in the X-Men film series and various television shows.

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_69f3495961d88190b16ea542c2c5f825 completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69f6d87e351c8190bc650bc2fd60ebde completed May 3, 2026, 5:09 a.m.
NED1 Entity disambiguation (via context triple) batch_6a351622bb9c81908f87ababac507164 completed June 19, 2026, 10:12 a.m.
NEDg Description generation batch_6a35176f4f788190ab662f3747247488 completed June 19, 2026, 10:18 a.m.
NED2 Entity disambiguation (via description) batch_6a3517fa9fc08190ad46f97546c7bae2 completed June 19, 2026, 10:20 a.m.
Created at: May 1, 2026, 1:28 a.m.