Triple

T14286804
Position Surface form Disambiguated ID Type / Status
Subject Professor Yana E354195 entity
Predicate alsoKnownAs P39 FINISHED
Object Yana
Yana is a professor character from the Doctor Who universe whose true identity is central to a major plot twist.
 E1090762 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: Yana | Statement: [Professor Yana, alsoKnownAs, Yana]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Yana
Context triple: [Professor Yana, alsoKnownAs, Yana]
  • A. Yani
    Yani is an Indonesian surname most notably associated with General Ahmad Yani, a national hero and high-ranking military officer killed during the 1965 coup attempt.
  • B. Yua
    Yua is a small genus of flowering plants in the grape family Vitaceae, native to parts of East Asia.
  • C. Yanaoca
    Yanaoca is a small Andean town in southern Peru that serves as the administrative and commercial center of Canas Province in the Cusco Region.
  • D. Yanayeva
    Yanayeva is a Slavic feminine surname, typically the female form of the Russian surname Yanayev.
  • E. Tenea
    Tenea was an ancient Greek city, traditionally associated with Corinthian colonists and mythic Trojan origins, known from classical sources and archaeological discoveries in the Peloponnese.
  • 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: Yana
Triple: [Professor Yana, alsoKnownAs, Yana]
Generated description
Yana is a professor character from the Doctor Who universe whose true identity is central to a major plot twist.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Yana
Target entity description: Yana is a professor character from the Doctor Who universe whose true identity is central to a major plot twist.
  • A. Yani
    Yani is an Indonesian surname most notably associated with General Ahmad Yani, a national hero and high-ranking military officer killed during the 1965 coup attempt.
  • B. Yua
    Yua is a small genus of flowering plants in the grape family Vitaceae, native to parts of East Asia.
  • C. Yanaoca
    Yanaoca is a small Andean town in southern Peru that serves as the administrative and commercial center of Canas Province in the Cusco Region.
  • D. Yanayeva
    Yanayeva is a Slavic feminine surname, typically the female form of the Russian surname Yanayev.
  • E. Tenea
    Tenea was an ancient Greek city, traditionally associated with Corinthian colonists and mythic Trojan origins, known from classical sources and archaeological discoveries in the Peloponnese.
  • 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_69d8278e17088190b328c5a9d4be74ff completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de697ef40c8190bea37724b28c2e99 completed April 14, 2026, 4:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd3d1c4d988190b595e6a33ef96c28 completed May 8, 2026, 1:32 a.m.
NEDg Description generation batch_69fd3da6f0648190876dd86dd51e72cc completed May 8, 2026, 1:34 a.m.
NED2 Entity disambiguation (via description) batch_69fd3e30868481908b55b368ab45c7fb completed May 8, 2026, 1:36 a.m.
Created at: April 10, 2026, 1:11 a.m.