Triple

T10325619
Position Surface form Disambiguated ID Type / Status
Subject Tully E242753 entity
Predicate character P662 FINISHED
Object Marlo
Marlo is a fictional character associated with Tully, likely appearing in a narrative centered on that figure.
 E857066 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: Marlo | Statement: [Tully, character, Marlo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Marlo
Context triple: [Tully, character, Marlo]
  • A. Marlohe
    Marlohe is the surname of French actress and model Bérénice Marlohe, best known for her role as Sévérine in the James Bond film "Skyfall."
  • B. Marla
    Marla is a feminine given name most notably borne by American actress and television personality Marla Maples.
  • C. Marylou
    Marylou is a free-spirited, impulsive young woman who embodies the restless, hedonistic energy of the Beat Generation in Jack Kerouac’s novel "On the Road."
  • D. Marli
    Marli is a given name commonly used as a feminine first name in various cultures.
  • E. Margi
    Margi are an ethnic group native to northeastern Nigeria, particularly in and around Adamawa State, known for their distinct language and cultural traditions.
  • 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: Marlo
Triple: [Tully, character, Marlo]
Generated description
Marlo is a fictional character associated with Tully, likely appearing in a narrative centered on that figure.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Marlo
Target entity description: Marlo is a fictional character associated with Tully, likely appearing in a narrative centered on that figure.
  • A. Marlohe
    Marlohe is the surname of French actress and model Bérénice Marlohe, best known for her role as Sévérine in the James Bond film "Skyfall."
  • B. Marla
    Marla is a feminine given name most notably borne by American actress and television personality Marla Maples.
  • C. Marylou
    Marylou is a free-spirited, impulsive young woman who embodies the restless, hedonistic energy of the Beat Generation in Jack Kerouac’s novel "On the Road."
  • D. Marli
    Marli is a given name commonly used as a feminine first name in various cultures.
  • E. Margi
    Margi are an ethnic group native to northeastern Nigeria, particularly in and around Adamawa State, known for their distinct language and cultural traditions.
  • 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_69d381af787481908bc401325c760a88 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4d7cd76348190b93562112300acfc completed April 7, 2026, 10:09 a.m.
NED1 Entity disambiguation (via context triple) batch_69d7503f3df88190bc5acb5e5295f787 completed April 9, 2026, 7:07 a.m.
NEDg Description generation batch_69d7516a4d088190b3e3b86956b6b821 completed April 9, 2026, 7:12 a.m.
NED2 Entity disambiguation (via description) batch_69d75200eecc819094e261c9fa7c75f5 completed April 9, 2026, 7:15 a.m.
Created at: April 6, 2026, 11:51 a.m.