Triple

T2181809
Position Surface form Disambiguated ID Type / Status
Subject Esra Erdoğan E49060 entity
Predicate givenName P17 FINISHED
Object Esra
Esra is a feminine given name commonly used in Turkey and other countries with Islamic cultural influence.
 E243173 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: Esra | Statement: [Esra Erdoğan, givenName, Esra]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Esra
Context triple: [Esra Erdoğan, givenName, Esra]
  • A. Emine
    Emine is a Turkish feminine given name commonly borne by women, including prominent public figures in Turkey.
  • B. Gulnare
    Gulnare is a central female character in Lord Byron’s narrative poem "The Corsair," known for her courage, passion, and pivotal role in the story’s dramatic events.
  • C. Seyhun
    Seyhun is the historical name used in Islamic and Central Asian sources for the Syr Darya River, one of the major rivers of Central Asia.
  • D. Sahra
    Sahra is a feminine given name, commonly considered a variant spelling of Sara or Sarah used in various cultures.
  • E. Handan
    Handan is a historic industrial city in southern Hebei Province, China, known as a former capital of the ancient State of Zhao and an important regional transportation and manufacturing hub.
  • 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: Esra
Triple: [Esra Erdoğan, givenName, Esra]
Generated description
Esra is a feminine given name commonly used in Turkey and other countries with Islamic cultural influence.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Esra
Target entity description: Esra is a feminine given name commonly used in Turkey and other countries with Islamic cultural influence.
  • A. Emine
    Emine is a Turkish feminine given name commonly borne by women, including prominent public figures in Turkey.
  • B. Gulnare
    Gulnare is a central female character in Lord Byron’s narrative poem "The Corsair," known for her courage, passion, and pivotal role in the story’s dramatic events.
  • C. Seyhun
    Seyhun is the historical name used in Islamic and Central Asian sources for the Syr Darya River, one of the major rivers of Central Asia.
  • D. Sahra
    Sahra is a feminine given name, commonly considered a variant spelling of Sara or Sarah used in various cultures.
  • E. Handan
    Handan is a historic industrial city in southern Hebei Province, China, known as a former capital of the ancient State of Zhao and an important regional transportation and manufacturing hub.
  • 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_69a88aa72d348190a9544bb5b8a4e71d completed March 4, 2026, 7:40 p.m.
NER Named-entity recognition batch_69abbef1f8c0819084da6002035bbf93 completed March 7, 2026, 6 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae5da786e48190896cae8fdcee3d83 completed March 9, 2026, 5:41 a.m.
NEDg Description generation batch_69ae5e30a69c8190a3f77e784401f671 completed March 9, 2026, 5:44 a.m.
NED2 Entity disambiguation (via description) batch_69ae5ea7909c8190a93d87a5d07b84d4 completed March 9, 2026, 5:46 a.m.
Created at: March 4, 2026, 7:45 p.m.