Triple

T880683
Position Surface form Disambiguated ID Type / Status
Subject Marija Ružić Marić E19018 entity
Predicate givenName P17 FINISHED
Object Marija
Marija is a feminine given name commonly used in Slavic and other European cultures, equivalent to "Maria" or "Mary."
E108095 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: Marija | Statement: [Marija Ružić Marić, givenName, Marija]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Marija
Context triple: [Marija Ružić Marić, givenName, Marija]
  • A. Klaudija
    Klaudija is a feminine given name, commonly used in Slavic countries, that corresponds to the name Claudia.
  • B. Mila
    Mila is a leading artificial intelligence research institute based in Quebec, renowned for its work in deep learning and machine learning.
  • C. Marić
    Marić is the Serbian family name of Mileva Marić, a pioneering physicist and mathematician known for her association with Albert Einstein.
  • D. Marija Pejčinović Burić
    Marija Pejčinović Burić is a Croatian diplomat and politician who has served as Secretary General of the Council of Europe.
  • E. Tanja Stomporowski
    Tanja Stomporowski is a German local politician who serves as the mayor of the town of Quakenbrück in Lower Saxony.
  • 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: Marija
Triple: [Marija Ružić Marić, givenName, Marija]
Generated description
Marija is a feminine given name commonly used in Slavic and other European cultures, equivalent to "Maria" or "Mary."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Marija
Target entity description: Marija is a feminine given name commonly used in Slavic and other European cultures, equivalent to "Maria" or "Mary."
  • A. Klaudija
    Klaudija is a feminine given name, commonly used in Slavic countries, that corresponds to the name Claudia.
  • B. Mila
    Mila is a leading artificial intelligence research institute based in Quebec, renowned for its work in deep learning and machine learning.
  • C. Marić
    Marić is the Serbian family name of Mileva Marić, a pioneering physicist and mathematician known for her association with Albert Einstein.
  • D. Marija Pejčinović Burić
    Marija Pejčinović Burić is a Croatian diplomat and politician who has served as Secretary General of the Council of Europe.
  • E. Tanja Stomporowski
    Tanja Stomporowski is a German local politician who serves as the mayor of the town of Quakenbrück in Lower Saxony.
  • 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_69a4939c32488190a7ccd41cf0abb22b completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4accb653c81909fe0753f78145be9 completed March 1, 2026, 9:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69a7cf55fec481908460522373cc48ba completed March 4, 2026, 6:21 a.m.
NEDg Description generation batch_69a7cfecba308190ae966e2e28e474b0 completed March 4, 2026, 6:23 a.m.
NED2 Entity disambiguation (via description) batch_69a7d041893881908a2dbf7af521db4d completed March 4, 2026, 6:25 a.m.
Created at: March 1, 2026, 7:39 p.m.