Triple

T7303716
Position Surface form Disambiguated ID Type / Status
Subject Hulusi Behçet E167921 entity
Predicate givenName P17 FINISHED
Object Hulusi
Hulusi is a Turkish physician best known for first describing Behçet's disease, a chronic inflammatory disorder that now bears his name.
 E654527 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: Hulusi | Statement: [Hulusi Behçet, givenName, Hulusi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Hulusi
Context triple: [Hulusi Behçet, givenName, Hulusi]
  • A. Hulusi Bey
    Hulusi Bey was a Turkish sports figure best known as the founder of the Ankara-based football club Gençlerbirliği S.K.
  • B. Musi
    Musi is a river in Indonesia that flows through the city of Palembang in South Sumatra and serves as an important transportation and economic waterway.
  • C. Husan
    Husan is a Palestinian village located in the Bethlehem Governorate of the West Bank.
  • D. Razihi
    Razihi is a highly divergent Arabic-related language spoken by a small community in the mountainous Jabal Razih region of northwestern Yemen.
  • E. Rushan
    Rushan is a county-level coastal city in eastern Shandong Province, China, known for its fishing industry, beaches, and marine-based economy.
  • 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: Hulusi
Triple: [Hulusi Behçet, givenName, Hulusi]
Generated description
Hulusi is a Turkish physician best known for first describing Behçet's disease, a chronic inflammatory disorder that now bears his name.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Hulusi
Target entity description: Hulusi is a Turkish physician best known for first describing Behçet's disease, a chronic inflammatory disorder that now bears his name.
  • A. Hulusi Bey
    Hulusi Bey was a Turkish sports figure best known as the founder of the Ankara-based football club Gençlerbirliği S.K.
  • B. Musi
    Musi is a river in Indonesia that flows through the city of Palembang in South Sumatra and serves as an important transportation and economic waterway.
  • C. Husan
    Husan is a Palestinian village located in the Bethlehem Governorate of the West Bank.
  • D. Razihi
    Razihi is a highly divergent Arabic-related language spoken by a small community in the mountainous Jabal Razih region of northwestern Yemen.
  • E. Rushan
    Rushan is a county-level coastal city in eastern Shandong Province, China, known for its fishing industry, beaches, and marine-based economy.
  • 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_69c6888c820881909fc68f689fe1c251 completed March 27, 2026, 1:39 p.m.
NER Named-entity recognition batch_69c6ebb352ec8190846eff044e08805e completed March 27, 2026, 8:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7e55bfccc8190a46067c60c3c1a3f completed March 28, 2026, 2:27 p.m.
NEDg Description generation batch_69c7e5fbe8a8819083a892f4e54013eb completed March 28, 2026, 2:30 p.m.
NED2 Entity disambiguation (via description) batch_69c7e69ca1ac8190a398da894c6cc04e completed March 28, 2026, 2:33 p.m.
Created at: March 27, 2026, 3:01 p.m.