Triple

T3760138
Position Surface form Disambiguated ID Type / Status
Subject When Harry Met Sally... E82140 entity
Predicate character P662 FINISHED
Object Jess
Jess is a supporting character in the romantic comedy film "When Harry Met Sally..." who serves as Harry’s best friend and provides comic relief and relationship advice.
 E385770 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: Jess | Statement: [When Harry Met Sally..., character, Jess]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Jess
Context triple: [When Harry Met Sally..., character, Jess]
  • A. Jennifer
    Jennifer is a common feminine given name of English origin, derived from the Cornish form of Guinevere and widely used in many English-speaking countries.
  • B. Jane
    Jane is a feminine given name of English origin that has been widely used in many English-speaking countries for centuries.
  • C. Jane
    Jane is a powerful vampire in the Twilight series, known for her childlike appearance and her ability to inflict excruciating pain with her mind as a high-ranking enforcer of the Volturi.
  • D. Jessica
    Jessica Barth is an American actress best known for her comedic role as Tami-Lynn in the "Ted" film series.
  • E. Jessica
    Jessica is a women's fashion and apparel brand that was sold exclusively through Sears Canada.
  • 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: Jess
Triple: [When Harry Met Sally..., character, Jess]
Generated description
Jess is a supporting character in the romantic comedy film "When Harry Met Sally..." who serves as Harry’s best friend and provides comic relief and relationship advice.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Jess
Target entity description: Jess is a supporting character in the romantic comedy film "When Harry Met Sally..." who serves as Harry’s best friend and provides comic relief and relationship advice.
  • A. Jennifer
    Jennifer is a common feminine given name of English origin, derived from the Cornish form of Guinevere and widely used in many English-speaking countries.
  • B. Jane
    Jane is a feminine given name of English origin that has been widely used in many English-speaking countries for centuries.
  • C. Jane
    Jane is a powerful vampire in the Twilight series, known for her childlike appearance and her ability to inflict excruciating pain with her mind as a high-ranking enforcer of the Volturi.
  • D. Jessica
    Jessica Barth is an American actress best known for her comedic role as Tami-Lynn in the "Ted" film series.
  • E. Jessica
    Jessica is a women's fashion and apparel brand that was sold exclusively through Sears Canada.
  • 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_69ad8b1db40081908b61ffa6b78afd4d completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69adcbc3d3f48190974cec104080949f completed March 8, 2026, 7:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4e5172abc81909cfa709ea866dc57 completed March 14, 2026, 4:33 a.m.
NEDg Description generation batch_69b4e65f868c819099b4fbf129773e6d completed March 14, 2026, 4:38 a.m.
NED2 Entity disambiguation (via description) batch_69b4e6bc95cc8190852b833e111cd889 completed March 14, 2026, 4:40 a.m.
Created at: March 8, 2026, 3:35 p.m.