Triple

T16064522
Position Surface form Disambiguated ID Type / Status
Subject Dafne Keen E389698 entity
Predicate notableWork P4 FINISHED
Object Logan
Logan is a 2017 superhero film in the X-Men franchise that follows an aging Wolverine in a gritty, character-driven story often praised as one of the best comic-book movies ever made.
 E187033 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: Logan | Statement: [Dafne Keen, notableWork, Logan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Logan
Context triple: [Dafne Keen, notableWork, Logan]
  • A. Logan
    Logan is a small village in eastern New Mexico, United States, known for its proximity to Ute Lake and its role as a local recreational and service hub in Quay County.
  • B. Logan
    Logan is a neighborhood in Wyoming, Ohio, that serves as the community surrounding the Wyoming train station.
  • C. Logan
    Logan is a city in northern Utah known for being home to Utah State University and for its scenic Cache Valley setting.
  • D. Logan
    Logan is a small village located within the council area of East Ayrshire in southwest Scotland.
  • E. Logan
    Logan is a residential neighborhood in North Philadelphia, Pennsylvania, known for its rowhouses and proximity to institutions like La Salle University.
  • 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: Logan
Triple: [Dafne Keen, notableWork, Logan]
Generated description
Logan is a 2017 superhero film in the X-Men franchise that follows an aging Wolverine in a gritty, character-driven story often praised as one of the best comic-book movies ever made.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Logan
Target entity description: Logan is a 2017 superhero film in the X-Men franchise that follows an aging Wolverine in a gritty, character-driven story often praised as one of the best comic-book movies ever made.
  • A. Logan chosen
    Logan is a 2017 superhero film in the X-Men franchise that follows an aging Wolverine on a violent, character-driven road journey in a bleak near-future.
  • B. Logan
    Logan is a Marvel Comics antihero better known as Wolverine, a mutant with retractable claws and a powerful healing factor who was subjected to the Weapon X program.
  • C. Logan
    Logan is a Marvel Comics limited series written by Brian K. Vaughan that explores Wolverine’s past and emotional depth beyond traditional superhero action.
  • D. Logan
    Logan is a name commonly used as both a given name and surname in English-speaking countries.
  • E. Logan
    Logan was an 18th-century Mingo (Native American) leader known for his initial friendship with colonial settlers and his later famous speech lamenting the murder of his family during escalating frontier conflicts.
  • F. None of above.

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_69d86daf32ec8190a8c0466c8f49c3c0 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e1837b048881908326739bbede756f completed April 17, 2026, 12:48 a.m.
NED1 Entity disambiguation (via context triple) batch_69ffe47ef6648190bf1fe216e78ef660 completed May 10, 2026, 1:50 a.m.
NEDg Description generation batch_69ffe5a4edfc8190831ddf8a4601764e completed May 10, 2026, 1:55 a.m.
NED2 Entity disambiguation (via description) batch_69ffe687c204819092a4a8de0b9d624d completed May 10, 2026, 1:59 a.m.
Created at: April 10, 2026, 4:57 a.m.