Triple

T16854923
Position Surface form Disambiguated ID Type / Status
Subject Ana Ularu E409758 entity
Predicate portrayedCharacter P1668 FINISHED
Object Vayentha
Vayentha is a ruthless assassin and primary antagonist in Dan Brown's novel and film adaptation "Inferno."
 E1237567 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: Vayentha | Statement: [Ana Ularu, portrayedCharacter, Vayentha]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Vayentha
Context triple: [Ana Ularu, portrayedCharacter, Vayentha]
  • A. Ve’nari
    Ve’nari is a mysterious and resourceful broker in World of Warcraft: Shadowlands, known for aiding players within the perilous endgame zone of the Maw.
  • B. Velathri
    Velathri is the ancient Etruscan city that later became known as Volterra, located in present-day Tuscany, Italy.
  • C. Teurnia
    Teurnia was an important ancient Roman city that served as a major administrative and cultural center in the province of Noricum, located in what is now southern Austria.
  • D. Menrva
    Menrva is an Etruscan goddess of wisdom, war, and the arts, closely associated with and a precursor to the Roman goddess Minerva.
  • E. Valaquenta
    Valaquenta is a section of J.R.R. Tolkien’s legendarium that provides a mythological account of the Valar, the Maiar, and the cosmology of Middle-earth.
  • 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: Vayentha
Triple: [Ana Ularu, portrayedCharacter, Vayentha]
Generated description
Vayentha is a ruthless assassin and primary antagonist in Dan Brown's novel and film adaptation "Inferno."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Vayentha
Target entity description: Vayentha is a ruthless assassin and primary antagonist in Dan Brown's novel and film adaptation "Inferno."
  • A. Ve’nari
    Ve’nari is a mysterious and resourceful broker in World of Warcraft: Shadowlands, known for aiding players within the perilous endgame zone of the Maw.
  • B. Velathri
    Velathri is the ancient Etruscan city that later became known as Volterra, located in present-day Tuscany, Italy.
  • C. Teurnia
    Teurnia was an important ancient Roman city that served as a major administrative and cultural center in the province of Noricum, located in what is now southern Austria.
  • D. Menrva
    Menrva is an Etruscan goddess of wisdom, war, and the arts, closely associated with and a precursor to the Roman goddess Minerva.
  • E. Valaquenta
    Valaquenta is a section of J.R.R. Tolkien’s legendarium that provides a mythological account of the Valar, the Maiar, and the cosmology of Middle-earth.
  • 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_69d88395e6c88190b22730f335107c14 completed April 10, 2026, 4:59 a.m.
NER Named-entity recognition batch_69e3b37c6e808190975b14b228253029 completed April 18, 2026, 4:38 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00c2a6f2c48190874839f78f943fdb completed May 10, 2026, 5:38 p.m.
NEDg Description generation batch_6a00c3812c5881908e5c897a5b824e26 completed May 10, 2026, 5:42 p.m.
NED2 Entity disambiguation (via description) batch_6a00c3f62f3881909dc5665db8433063 completed May 10, 2026, 5:44 p.m.
Created at: April 10, 2026, 5:24 a.m.