Triple

T11313948
Position Surface form Disambiguated ID Type / Status
Subject Rose Leslie E267913 entity
Predicate notableWork P4 FINISHED
Object Morgan
Morgan is a 2016 science fiction horror film about a genetically engineered human hybrid whose violent behavior leads to a crisis among the scientists who created her.
 E305194 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: Morgan | Statement: [Rose Leslie, notableWork, Morgan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Morgan
Context triple: [Rose Leslie, notableWork, Morgan]
  • A. Morgan
    Morgan is the middle name of the renowned English novelist and essayist E. M. Forster.
  • B. Morgan
    Morgan is a Chicago 'L' rapid transit station on the Near West Side serving the city's Pink and Green Lines.
  • C. Morgan
    Morgan is a given name used by the American documentary filmmaker Morgan Neville.
  • D. Morgan
    Morgan is a prominent law firm that forms part of the larger legal practice Lloyd, Morgan & Jones.
  • E. Morgan
    Morgan is the party that successfully defended the constitutionality of a key provision of the Voting Rights Act in the landmark U.S. Supreme Court case Katzenbach v. Morgan.
  • 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: Morgan
Triple: [Rose Leslie, notableWork, Morgan]
Generated description
Morgan is a 2016 science fiction horror film about a genetically engineered human hybrid whose violent behavior leads to a crisis among the scientists who created her.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Morgan
Target entity description: Morgan is a 2016 science fiction horror film about a genetically engineered human hybrid whose violent behavior leads to a crisis among the scientists who created her.
  • A. Morgan chosen
    Morgan is a 2016 science fiction horror film about a genetically engineered human hybrid whose violent behavior leads to a crisis among the scientists who created her.
  • B. Morgan
    Morgan is a given name used by the American documentary filmmaker Morgan Neville.
  • C. Morgan
    Morgan is the second-generation core design used in AMD's Duron processors, featuring improvements in performance and efficiency over the original Spitfire core.
  • D. Morgan
    Morgan is a common English surname borne by numerous notable individuals across fields such as science, finance, and entertainment.
  • E. Morgan
    Morgan is the first-generation microarchitecture used in AMD's Duron processors, designed as a budget-friendly derivative of the Athlon line with reduced cache and simplified features.
  • 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_69d6aaca5c24819083db46a30d86cb34 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e9c2c7b081909af8acebc8aa93aa completed April 9, 2026, 6:02 p.m.
NED1 Entity disambiguation (via context triple) batch_69e50a910a2c8190b8afd4c988e64141 completed April 19, 2026, 5:02 p.m.
NEDg Description generation batch_69e5167083fc8190b5cdbfca5b25b295 completed April 19, 2026, 5:52 p.m.
NED2 Entity disambiguation (via description) batch_69e51795f024819086bfe7deba485fdd completed April 19, 2026, 5:57 p.m.
Created at: April 8, 2026, 9:32 p.m.