Triple

T12616875
Position Surface form Disambiguated ID Type / Status
Subject Rachel Zane E301276 entity
Predicate worksAt P7 FINISHED
Object Pearson Specter
Pearson Specter is a prestigious New York City law firm in the television series "Suits," known for its high-stakes corporate cases and powerful attorneys.
E992076 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: Pearson Specter | Statement: [Rachel Zane, worksAt, Pearson Specter]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pearson Specter
Context triple: [Rachel Zane, worksAt, Pearson Specter]
  • A. Dani Dennison
    Dani Dennison is a brave and spirited young girl in the film "Hocus Pocus" who helps her brother battle resurrected witches on Halloween night.
  • B. Chris DeWolfe
    Chris DeWolfe is an American entrepreneur best known as the co-creator and former CEO of the pioneering social networking site MySpace.
  • C. Chris Parker
    Chris Parker is a video game developer and producer best known as one of the founders of the role-playing game studio Obsidian Entertainment.
  • D. Alec Scudder
    Alec Scudder is a working-class gamekeeper who becomes the passionate love interest of the title character in E.M. Forster’s novel "Maurice."
  • E. Sam Harper
    Sam Harper is a screenwriter best known for writing family-friendly comedy films such as "Cheaper by the Dozen" and "Freaky Friday."
  • 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: Pearson Specter
Triple: [Rachel Zane, worksAt, Pearson Specter]
Generated description
Pearson Specter is a prestigious New York City law firm in the television series "Suits," known for its high-stakes corporate cases and powerful attorneys.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Pearson Specter
Target entity description: Pearson Specter is a prestigious New York City law firm in the television series "Suits," known for its high-stakes corporate cases and powerful attorneys.
  • A. Dani Dennison
    Dani Dennison is a brave and spirited young girl in the film "Hocus Pocus" who helps her brother battle resurrected witches on Halloween night.
  • B. Chris DeWolfe
    Chris DeWolfe is an American entrepreneur best known as the co-creator and former CEO of the pioneering social networking site MySpace.
  • C. Chris Parker
    Chris Parker is a video game developer and producer best known as one of the founders of the role-playing game studio Obsidian Entertainment.
  • D. Alec Scudder
    Alec Scudder is a working-class gamekeeper who becomes the passionate love interest of the title character in E.M. Forster’s novel "Maurice."
  • E. Sam Harper
    Sam Harper is a screenwriter best known for writing family-friendly comedy films such as "Cheaper by the Dozen" and "Freaky Friday."
  • 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_69d7bdeaf49c8190b13800111fa77ea3 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d960c63ea48190ae1aae9280a023a6 completed April 10, 2026, 8:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69f65ed2e12c819097cfd2a40116f491 completed May 2, 2026, 8:30 p.m.
NEDg Description generation batch_69f65fb03b248190b264230b84b17635 completed May 2, 2026, 8:33 p.m.
NED2 Entity disambiguation (via description) batch_69f6608ec32c8190803c6f5677c300d6 completed May 2, 2026, 8:37 p.m.
Created at: April 9, 2026, 5:13 p.m.