Triple

T16291326
Position Surface form Disambiguated ID Type / Status
Subject OOSA E395528 entity
Predicate appliesTo P1129 FINISHED
Object Salalah Airport E92455 NE FINISHED

How this triple was built (2 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: Salalah Airport | Statement: [OOSA, appliesTo, Salalah Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Salalah Airport
Context triple: [OOSA, appliesTo, Salalah Airport]
  • A. Salalah Airport chosen
    Salalah Airport is a major international airport in southern Oman serving the city of Salalah and the Dhofar region, handling both domestic and regional flights and supporting the area’s tourism and trade.
  • B. Sanaʽa International Airport
    Sanaʽa International Airport is the main commercial airport serving Yemen’s capital, handling both domestic and international flights.
  • C. Aden Adde International Airport
    Aden Adde International Airport is the main international airport serving Mogadishu and the primary aviation gateway to Somalia.
  • D. Red Sea International Airport
    Red Sea International Airport is a modern airport in Saudi Arabia built to serve the luxury tourism and development hub of the Red Sea Project on the kingdom’s western coast.
  • E. Al Ghaydah Airport
    Al Ghaydah Airport is a regional civilian airport serving the city of Al Ghaydah and the surrounding Mahra Governorate in eastern Yemen.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d87f22c7248190a54c949738441e2e completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e2491821d0819086cffdd7551ba85a completed April 17, 2026, 2:52 p.m.
NED1 Entity disambiguation (via context triple) batch_6a003550fc0c8190ba78666da8b3cd81 completed May 10, 2026, 7:35 a.m.
Created at: April 10, 2026, 5:05 a.m.