Triple

T17590429
Position Surface form Disambiguated ID Type / Status
Subject البيضاء E428431 entity
Predicate near P350 FINISHED
Object مدينة درنة
مدينة درنة هي مدينة ساحلية في شرق ليبيا تقع على البحر المتوسط وتُعرف بكونها مركزاً حضرياً وتجارياً مهماً في إقليم برقة.
 E1276979 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: مدينة درنة | Statement: [البيضاء, near, مدينة درنة]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: مدينة درنة
Context triple: [البيضاء, near, مدينة درنة]
  • A. Tripoli
    Tripoli is a historic city in the central Peloponnese of Greece that serves as the main urban and administrative center of the Arcadia region.
  • B. Tripoli
    Tripoli is a historic Mediterranean port city that serves as the capital and largest urban center of Libya.
  • C. Tripoli
    Tripoli was a historic American shipyard and port city involved in constructing naval vessels such as the USS Intrepid.
  • D. Tripoli
    Tripoli is Lebanon’s second-largest city, a historic Mediterranean port known for its medieval Mamluk architecture and vibrant commercial life.
  • E. Bengasi
    Bengasi is a metro station on the Turin Metro system in Turin, Italy.
  • 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: مدينة درنة
Triple: [البيضاء, near, مدينة درنة]
Generated description
مدينة درنة هي مدينة ساحلية في شرق ليبيا تقع على البحر المتوسط وتُعرف بكونها مركزاً حضرياً وتجارياً مهماً في إقليم برقة.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: مدينة درنة
Target entity description: مدينة درنة هي مدينة ساحلية في شرق ليبيا تقع على البحر المتوسط وتُعرف بكونها مركزاً حضرياً وتجارياً مهماً في إقليم برقة.
  • A. Tripoli
    Tripoli is a historic Mediterranean port city that serves as the capital and largest urban center of Libya.
  • B. Tripoli
    Tripoli is a historic city in the central Peloponnese of Greece that serves as the main urban and administrative center of the Arcadia region.
  • C. Tripoli
    Tripoli was a historic American shipyard and port city involved in constructing naval vessels such as the USS Intrepid.
  • D. Tripoli
    Tripoli is Lebanon’s second-largest city, a historic Mediterranean port known for its medieval Mamluk architecture and vibrant commercial life.
  • E. Bengasi
    Bengasi is a metro station on the Turin Metro system in Turin, Italy.
  • 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_69d889e1030481909950e140c63255b9 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e469e6e3888190b73a5b6d7e8c0a55 completed April 19, 2026, 5:36 a.m.
NED1 Entity disambiguation (via context triple) batch_6a01ddf59fd881909bb89d4e4d173094 completed May 11, 2026, 1:47 p.m.
NEDg Description generation batch_6a01e3373b408190a507a4aba1a74a8f completed May 11, 2026, 2:09 p.m.
NED2 Entity disambiguation (via description) batch_6a01e3b1bcb481908b43099705a728f3 completed May 11, 2026, 2:12 p.m.
Created at: April 10, 2026, 5:51 a.m.