Triple

T10833200
Position Surface form Disambiguated ID Type / Status
Subject Marc-Édouard Vlasic E255675 entity
Predicate hasNickname P39 FINISHED
Object Pickles
Pickles is the nickname of Marc-Édouard Vlasic, a Canadian professional ice hockey defenseman known for his long NHL career, primarily with the San Jose Sharks.
 E888307 NE FINISHED

How this triple was built (4 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: Pickles | Statement: [Marc-Édouard Vlasic, hasNickname, Pickles]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Pickles
Context triple: [Marc-Édouard Vlasic, hasNickname, Pickles]
  • A. Capers
    Capers is a surname most notably associated with Dom Capers, an American football coach known for his roles as an NFL head coach and defensive coordinator.
  • B. Pickle
    Pickle is a surname most notably associated with Jake Pickle, a long-serving U.S. Congressman from Texas.
  • C. Mustard
    Mustard is an American record producer and DJ known for his minimalist, club-oriented West Coast hip hop sound and numerous chart-topping collaborations.
  • D. Salo
    Salo is a town in southwestern Finland known for its electronics industry history and location along the Salo River.
  • E. Utz
    Utz is a 1992 British drama film, based on Bruce Chatwin’s novel, about an eccentric porcelain collector in Communist-era Prague.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
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: Pickles
Triple: [Marc-Édouard Vlasic, hasNickname, Pickles]
Generated description
Pickles is the nickname of Marc-Édouard Vlasic, a Canadian professional ice hockey defenseman known for his long NHL career, primarily with the San Jose Sharks.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Pickles
Target entity description: Pickles is the nickname of Marc-Édouard Vlasic, a Canadian professional ice hockey defenseman known for his long NHL career, primarily with the San Jose Sharks.
  • A. Capers
    Capers is a surname most notably associated with Dom Capers, an American football coach known for his roles as an NFL head coach and defensive coordinator.
  • B. Pickle
    Pickle is a surname most notably associated with Jake Pickle, a long-serving U.S. Congressman from Texas.
  • C. Mustard
    Mustard is an American record producer and DJ known for his minimalist, club-oriented West Coast hip hop sound and numerous chart-topping collaborations.
  • D. Salo
    Salo is a town in southwestern Finland known for its electronics industry history and location along the Salo River.
  • E. Utz
    Utz is a 1992 British drama film, based on Bruce Chatwin’s novel, about an eccentric porcelain collector in Communist-era Prague.
  • F. None of above. chosen

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_69d6aa81a5d08190aa86689061d1ddd2 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d7442439dc8190af59f9c8d0637c01 completed April 9, 2026, 6:16 a.m.
NED1 Entity disambiguation (via context triple) batch_69de85aa5ea88190ab6399e46eba5c49 completed April 14, 2026, 6:21 p.m.
NEDg Description generation batch_69de8e70da448190b80068fea047a88c completed April 14, 2026, 6:58 p.m.
NED2 Entity disambiguation (via description) batch_69de94dd45548190a88b5ab991756d12 completed April 14, 2026, 7:26 p.m.
Created at: April 8, 2026, 9:19 p.m.