Triple

T17590430
Position Surface form Disambiguated ID Type / Status
Subject البيضاء E428431 entity
Predicate near P350 FINISHED
Object مدينة بنغازي
مدينة بنغازي هي ثاني أكبر مدن ليبيا ومركز اقتصادي وثقافي مهم يقع في إقليم برقة على ساحل البحر المتوسط.
 E1277429 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 بنغازي | Statement: [البيضاء, near, مدينة بنغازي]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: مدينة بنغازي
Context triple: [البيضاء, near, مدينة بنغازي]
  • A. Bengasi
    Bengasi is a metro station on the Turin Metro system in Turin, Italy.
  • B. Tripoli
    Tripoli was a historic American shipyard and port city involved in constructing naval vessels such as the USS Intrepid.
  • C. Tripoli
    Tripoli is a historic Mediterranean port city that serves as the capital and largest urban center of Libya.
  • D. 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.
  • E. Tripoli
    Tripoli is Lebanon’s second-largest city, a historic Mediterranean port known for its medieval Mamluk architecture and vibrant commercial life.
  • 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 بنغازي
Triple: [البيضاء, near, مدينة بنغازي]
Generated description
مدينة بنغازي هي ثاني أكبر مدن ليبيا ومركز اقتصادي وثقافي مهم يقع في إقليم برقة على ساحل البحر المتوسط.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: مدينة بنغازي
Target entity description: مدينة بنغازي هي ثاني أكبر مدن ليبيا ومركز اقتصادي وثقافي مهم يقع في إقليم برقة على ساحل البحر المتوسط.
  • A. Bengasi
    Bengasi is a metro station on the Turin Metro system in Turin, Italy.
  • B. Tripoli
    Tripoli was a historic American shipyard and port city involved in constructing naval vessels such as the USS Intrepid.
  • C. Tripoli
    Tripoli is a historic Mediterranean port city that serves as the capital and largest urban center of Libya.
  • D. Tripoli
    Tripoli is Lebanon’s second-largest city, a historic Mediterranean port known for its medieval Mamluk architecture and vibrant commercial life.
  • E. 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.
  • 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_6a01e815be708190a91ae8e6fc3f75d3 completed May 11, 2026, 2:30 p.m.
NEDg Description generation batch_6a01e8fe86e08190a2d1492a55cbbce0 completed May 11, 2026, 2:34 p.m.
NED2 Entity disambiguation (via description) batch_6a01e960d6888190981aa4db6df7c1cd completed May 11, 2026, 2:36 p.m.
Created at: April 10, 2026, 5:51 a.m.