Triple

T9107246
Position Surface form Disambiguated ID Type / Status
Subject المنطقة الشرقية E218507 entity
Predicate containsCity P294 FINISHED
Object رأس تنورة
رأس تنورة هي مدينة ساحلية وصناعية في شرق السعودية تُعد من أهم مراكز صناعة وتصدير النفط في المملكة.
 E777231 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: رأس تنورة | Statement: [المنطقة الشرقية, containsCity, رأس تنورة]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: رأس تنورة
Context triple: [المنطقة الشرقية, containsCity, رأس تنورة]
  • A. Trinetta
    Trinetta is a character from the animated television series "Who Asked You?," known for her distinctive personality and role in the show's comedic narrative.
  • B. Tamina
    The Tamina is a river in eastern Switzerland known for flowing through the deep Tamina Gorge before joining the Alpine Rhine.
  • C. Shira
    Shira is the eroded western volcanic cone and plateau of Mount Kilimanjaro, forming one of the mountain’s three main summits.
  • D. Terêna
    Terêna is an Arawakan language spoken by the Terena Indigenous people of Brazil, primarily in the state of Mato Grosso do Sul.
  • E. Katisha
    Katisha is a formidable, older noblewoman and comic villainess in Gilbert and Sullivan’s operetta "The Mikado," known for her dramatic presence and unrequited love for Nanki-Poo.
  • 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: رأس تنورة
Triple: [المنطقة الشرقية, containsCity, رأس تنورة]
Generated description
رأس تنورة هي مدينة ساحلية وصناعية في شرق السعودية تُعد من أهم مراكز صناعة وتصدير النفط في المملكة.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: رأس تنورة
Target entity description: رأس تنورة هي مدينة ساحلية وصناعية في شرق السعودية تُعد من أهم مراكز صناعة وتصدير النفط في المملكة.
  • A. Trinetta
    Trinetta is a character from the animated television series "Who Asked You?," known for her distinctive personality and role in the show's comedic narrative.
  • B. Tamina
    The Tamina is a river in eastern Switzerland known for flowing through the deep Tamina Gorge before joining the Alpine Rhine.
  • C. Shira
    Shira is the eroded western volcanic cone and plateau of Mount Kilimanjaro, forming one of the mountain’s three main summits.
  • D. Terêna
    Terêna is an Arawakan language spoken by the Terena Indigenous people of Brazil, primarily in the state of Mato Grosso do Sul.
  • E. Katisha
    Katisha is a formidable, older noblewoman and comic villainess in Gilbert and Sullivan’s operetta "The Mikado," known for her dramatic presence and unrequited love for Nanki-Poo.
  • 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_69ca83db7448819090d0a5de842ef2ac completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69cca573da0081909f4afcdb3ac99c53 completed April 1, 2026, 4:56 a.m.
NED1 Entity disambiguation (via context triple) batch_69d01842c12c81909fdb57b60e2f11f1 completed April 3, 2026, 7:42 p.m.
NEDg Description generation batch_69d0196766248190aebda80cbd7d1eef completed April 3, 2026, 7:47 p.m.
NED2 Entity disambiguation (via description) batch_69d019d76d2481909118b163ce5713f2 completed April 3, 2026, 7:49 p.m.
Created at: March 30, 2026, 7:16 p.m.