Triple

T1016284
Position Surface form Disambiguated ID Type / Status
Subject Verna Fields E21937 entity
Predicate notableWork P4 FINISHED
Object Targets
Targets is a 1968 American thriller film, often praised as an early landmark in New Hollywood cinema, that interweaves the story of an aging horror star with a modern-day sniper rampage.
 E121554 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: Targets | Statement: [Verna Fields, notableWork, Targets]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Targets
Context triple: [Verna Fields, notableWork, Targets]
  • A. Carrier
    Carrier is a leading global brand specializing in heating, ventilation, air conditioning (HVAC), and refrigeration solutions.
  • B. TNT
    TNT is an American cable television network known for airing sports, movies, and original drama programming.
  • C. The Limited
    The Limited is an American retail clothing brand and former mall-based specialty store chain known for its women’s apparel and its role in building the retail empire of the Wexner family.
  • D. Tarifit
    Tarifit is a Northern Berber language spoken primarily by the Riffian people in the Rif region of northern Morocco.
  • E. Tek
    Tek is a brand associated with Tektronix, known for electronic test and measurement equipment such as oscilloscopes and signal analyzers.
  • 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: Targets
Triple: [Verna Fields, notableWork, Targets]
Generated description
Targets is a 1968 American thriller film, often praised as an early landmark in New Hollywood cinema, that interweaves the story of an aging horror star with a modern-day sniper rampage.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Targets
Target entity description: Targets is a 1968 American thriller film, often praised as an early landmark in New Hollywood cinema, that interweaves the story of an aging horror star with a modern-day sniper rampage.
  • A. Carrier
    Carrier is a leading global brand specializing in heating, ventilation, air conditioning (HVAC), and refrigeration solutions.
  • B. TNT
    TNT is an American cable television network known for airing sports, movies, and original drama programming.
  • C. The Limited
    The Limited is an American retail clothing brand and former mall-based specialty store chain known for its women’s apparel and its role in building the retail empire of the Wexner family.
  • D. Tarifit
    Tarifit is a Northern Berber language spoken primarily by the Riffian people in the Rif region of northern Morocco.
  • E. Tek
    Tek is a brand associated with Tektronix, known for electronic test and measurement equipment such as oscilloscopes and signal analyzers.
  • 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_69a493c68e24819080ed0ee8bcfd5ce0 completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4b7c1e9d08190baf7e81f3777168d completed March 1, 2026, 10:03 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac3bb1b0bc819095af3b50bfebca1e completed March 7, 2026, 2:52 p.m.
NEDg Description generation batch_69ac3dd441fc8190ad462aa07e9c8c9b completed March 7, 2026, 3:01 p.m.
NED2 Entity disambiguation (via description) batch_69ac3e45d9e88190bc88d037c00c3ecc completed March 7, 2026, 3:03 p.m.
Created at: March 1, 2026, 7:41 p.m.