Triple

T2865048
Position Surface form Disambiguated ID Type / Status
Subject Kate Mara E63418 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: [Kate Mara, notableWork, Morgan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Morgan
Context triple: [Kate Mara, notableWork, Morgan]
  • A. Morgan
    Morgan is the middle name of the renowned English novelist and essayist E. M. Forster.
  • B. Morgan
    Morgan is a prominent American banking and finance family name most famously associated with financier J. P. Morgan and the powerful House of Morgan banking dynasty.
  • C. Morgan
    Morgan is a given name most famously associated with acclaimed American actor and narrator Morgan Freeman.
  • D. Hudson Fysh
    Hudson Fysh was an Australian aviator and businessman best known as a co-founder and long-serving leader of Qantas, helping to establish it as a major international airline.
  • E. Pearce
    Pearce is a surname and given name of English origin, commonly considered a variant of "Pierce" and ultimately derived from the name "Peter."
  • 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: [Kate Mara, 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
    Morgan is a prominent American banking and finance family name most famously associated with financier J. P. Morgan and the powerful House of Morgan banking dynasty.
  • B. Morgan
    Morgan is the middle name of the renowned English novelist and essayist E. M. Forster.
  • C. Morgan
    Morgan is a given name most famously associated with acclaimed American actor and narrator Morgan Freeman.
  • D. Hudson Fysh
    Hudson Fysh was an Australian aviator and businessman best known as a co-founder and long-serving leader of Qantas, helping to establish it as a major international airline.
  • E. Pearce
    Pearce is a surname and given name of English origin, commonly considered a variant of "Pierce" and ultimately derived from the name "Peter."
  • 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_69ab4c42fb8c8190b36e161d47c03b81 completed March 6, 2026, 9:50 p.m.
NER Named-entity recognition batch_69abdfb9e64c819087b1a47caeb174d5 completed March 7, 2026, 8:20 a.m.
NED1 Entity disambiguation (via context triple) batch_69b01da458ec8190ae07237d7e23b302 completed March 10, 2026, 1:33 p.m.
NEDg Description generation batch_69b01e45f6e481908665e0961c3a3778 completed March 10, 2026, 1:36 p.m.
NED2 Entity disambiguation (via description) batch_69b0224832108190be252c0245d588d2 completed March 10, 2026, 1:53 p.m.
Created at: March 6, 2026, 10:02 p.m.