Triple

T6449016
Position Surface form Disambiguated ID Type / Status
Subject Pickup on South Street E139815 entity
Predicate mainCharacter P1183 FINISHED
Object Zara
Zara is a character in the 1953 film noir "Pickup on South Street," involved in the story’s underworld of espionage and crime.
 E594459 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: Zara | Statement: [Pickup on South Street, mainCharacter, Zara]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Zara
Context triple: [Pickup on South Street, mainCharacter, Zara]
  • A. Zara
    Zara is the historical Italian name for the coastal Croatian city of Zadar on the Adriatic Sea.
  • B. H&M
    H&M is a global fast-fashion retail chain known for offering trendy clothing and accessories at affordable prices.
  • C. H&M
    H&M, in this context, refers to the historic Hudson and Manhattan Railroad, an early 20th-century rapid transit system that connected Manhattan with New Jersey and served as a predecessor to today’s PATH trains.
  • D. C&A
    C&A is a major international fashion retail chain known for offering affordable clothing and accessories across numerous European and global markets.
  • E. Uniqlo
    Uniqlo is a global Japanese clothing retailer known for its affordable, minimalist casual wear and functional basics.
  • 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: Zara
Triple: [Pickup on South Street, mainCharacter, Zara]
Generated description
Zara is a character in the 1953 film noir "Pickup on South Street," involved in the story’s underworld of espionage and crime.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Zara
Target entity description: Zara is a character in the 1953 film noir "Pickup on South Street," involved in the story’s underworld of espionage and crime.
  • A. Zara
    Zara is the historical Italian name for the coastal Croatian city of Zadar on the Adriatic Sea.
  • B. H&M
    H&M is a global fast-fashion retail chain known for offering trendy clothing and accessories at affordable prices.
  • C. H&M
    H&M, in this context, refers to the historic Hudson and Manhattan Railroad, an early 20th-century rapid transit system that connected Manhattan with New Jersey and served as a predecessor to today’s PATH trains.
  • D. C&A
    C&A is a major international fashion retail chain known for offering affordable clothing and accessories across numerous European and global markets.
  • E. Uniqlo
    Uniqlo is a global Japanese clothing retailer known for its affordable, minimalist casual wear and functional basics.
  • 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_69c008b301948190a35854e5284dc822 completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c069b1a61c81908610264c098d25b0 completed March 22, 2026, 10:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69c64bd290c481909f543cc03eee98bf completed March 27, 2026, 9:20 a.m.
NEDg Description generation batch_69c64dce99fc819089fbe8925dc52b7a completed March 27, 2026, 9:28 a.m.
NED2 Entity disambiguation (via description) batch_69c64e5cd1b88190abcdc8af02991d1d completed March 27, 2026, 9:31 a.m.
Created at: March 22, 2026, 4:47 p.m.