Triple

T5548572
Position Surface form Disambiguated ID Type / Status
Subject Mahmoud E145469 entity
Predicate hasVariant P455 FINISHED
Object Mahmut
Mahmut is a masculine given name commonly used in Turkish and related cultures, derived from the Arabic name Mahmoud.
 E532687 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: Mahmut | Statement: [Mahmoud, hasVariant, Mahmut]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Mahmut
Context triple: [Mahmoud, hasVariant, Mahmut]
  • A. Ahmet
    Ahmet is a common male given name of Arabic origin, widely used in Turkey and other Muslim-majority countries as a variant of Ahmed.
  • B. Mehmet
    Mehmet is a common Turkish male given name of Arabic origin, widely used across Turkey and among Turkish communities.
  • C. Mustafa
    Mustafa is the given birth name of Mustafa Kemal Atatürk, the founder and first president of the Republic of Turkey.
  • D. Murat
    Murat is a historic small town in south-central France, known for its volcanic landscape setting in the Cantal region and its traditional stone architecture.
  • E. Ziya
    Ziya is a masculine given name of Turkish origin, historically associated with notable figures such as sociologist and nationalist thinker Ziya Gökalp.
  • 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: Mahmut
Triple: [Mahmoud, hasVariant, Mahmut]
Generated description
Mahmut is a masculine given name commonly used in Turkish and related cultures, derived from the Arabic name Mahmoud.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Mahmut
Target entity description: Mahmut is a masculine given name commonly used in Turkish and related cultures, derived from the Arabic name Mahmoud.
  • A. Ahmet
    Ahmet is a common male given name of Arabic origin, widely used in Turkey and other Muslim-majority countries as a variant of Ahmed.
  • B. Mehmet
    Mehmet is a common Turkish male given name of Arabic origin, widely used across Turkey and among Turkish communities.
  • C. Mustafa
    Mustafa is the given birth name of Mustafa Kemal Atatürk, the founder and first president of the Republic of Turkey.
  • D. Murat
    Murat is a historic small town in south-central France, known for its volcanic landscape setting in the Cantal region and its traditional stone architecture.
  • E. Ziya
    Ziya is a masculine given name of Turkish origin, historically associated with notable figures such as sociologist and nationalist thinker Ziya Gökalp.
  • 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_69c008fb879c81909f5bfa56fadc1d46 completed March 22, 2026, 3:21 p.m.
NER Named-entity recognition batch_69c01fe143ec8190bb67d2530c92a419 completed March 22, 2026, 4:59 p.m.
NED1 Entity disambiguation (via context triple) batch_69c0282dd7408190ad762fca9ff5e04b completed March 22, 2026, 5:34 p.m.
NEDg Description generation batch_69c0400d14cc8190a9962cb43fe17d83 completed March 22, 2026, 7:16 p.m.
NED2 Entity disambiguation (via description) batch_69c0407964d08190b5552b410c9ba915 completed March 22, 2026, 7:18 p.m.
Created at: March 22, 2026, 3:35 p.m.