Triple

T14702531
Position Surface form Disambiguated ID Type / Status
Subject Tatjana E345340 entity
Predicate hasDiminutive P456 FINISHED
Object Tanja
Tanja is a feminine given name commonly used in various European countries, often as a variant of Tatjana or Tanya.
 E1115742 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: Tanja | Statement: [Tatjana, hasDiminutive, Tanja]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tanja
Context triple: [Tatjana, hasDiminutive, Tanja]
  • A. Nadiža
    Nadiža is a river in the western Balkans, known for its clear waters and scenic course through the mountainous border region between Slovenia and Italy.
  • B. Anja
    Anja is a feminine given name commonly used in various European countries, often considered a variant of Anna.
  • C. Dáša
    Dáša is a common Czech and Slovak feminine given name, typically used as a diminutive form of Dagmar.
  • D. Dunja
    Dunja is a feminine given name commonly used in South Slavic countries, often associated with the Bosnian human rights advocate Dunja Mijatović.
  • E. Corina
    Corina is a feminine given name used in various cultures, often considered a variant of names like Corine or Corinna.
  • 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: Tanja
Triple: [Tatjana, hasDiminutive, Tanja]
Generated description
Tanja is a feminine given name commonly used in various European countries, often as a variant of Tatjana or Tanya.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tanja
Target entity description: Tanja is a feminine given name commonly used in various European countries, often as a variant of Tatjana or Tanya.
  • A. Nadiža
    Nadiža is a river in the western Balkans, known for its clear waters and scenic course through the mountainous border region between Slovenia and Italy.
  • B. Anja
    Anja is a feminine given name commonly used in various European countries, often considered a variant of Anna.
  • C. Dáša
    Dáša is a common Czech and Slovak feminine given name, typically used as a diminutive form of Dagmar.
  • D. Dunja
    Dunja is a feminine given name commonly used in South Slavic countries, often associated with the Bosnian human rights advocate Dunja Mijatović.
  • E. Corina
    Corina is a feminine given name used in various cultures, often considered a variant of names like Corine or Corinna.
  • 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_69d822e4a8c08190a155df736bb7bc13 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69deb6071e5c8190bb5509c859135c2d completed April 14, 2026, 9:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69fdf0861c308190af0b5da403ecb321 completed May 8, 2026, 2:17 p.m.
NEDg Description generation batch_69fdf368782c8190825247435eab2045 completed May 8, 2026, 2:30 p.m.
NED2 Entity disambiguation (via description) batch_69fdf3fe50ac8190ad5529427472eda3 completed May 8, 2026, 2:32 p.m.
Created at: April 10, 2026, 1:28 a.m.