Triple

T10038716
Position Surface form Disambiguated ID Type / Status
Subject Wadi ad-Dawasir E205239 entity
Predicate airportIcaoCode P419 FINISHED
Object OEWD
OEWD is the ICAO airport code for Wadi ad-Dawasir Domestic Airport in Saudi Arabia.
 E837211 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: OEWD | Statement: [Wadi ad-Dawasir, airportIcaoCode, OEWD]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: OEWD
Context triple: [Wadi ad-Dawasir, airportIcaoCode, OEWD]
  • A. OEWD
    OEWD is a San Francisco city agency that promotes economic development, supports businesses, and advances workforce and job training initiatives for residents.
  • B. OKW
    OKW (Oberkommando der Wehrmacht) was the German Armed Forces High Command during Nazi Germany, overseeing the strategic direction and coordination of the Wehrmacht in World War II.
  • C. OWF
    OWF is the acronym for the Office of Wildland Fire, a U.S. federal office responsible for coordinating and overseeing wildland fire management policies and programs.
  • D. OSWP
    OSWP (Offensive Security Wireless Professional) is a specialized cybersecurity certification focused on assessing and exploiting vulnerabilities in wireless networks.
  • E. WEOG
    WEOG is the United Nations’ regional group for Western European and other like-minded states, used primarily for consultations and the allocation of seats in UN bodies.
  • 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: OEWD
Triple: [Wadi ad-Dawasir, airportIcaoCode, OEWD]
Generated description
OEWD is the ICAO airport code for Wadi ad-Dawasir Domestic Airport in Saudi Arabia.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: OEWD
Target entity description: OEWD is the ICAO airport code for Wadi ad-Dawasir Domestic Airport in Saudi Arabia.
  • A. OEWD
    OEWD is a San Francisco city agency that promotes economic development, supports businesses, and advances workforce and job training initiatives for residents.
  • B. OKW
    OKW (Oberkommando der Wehrmacht) was the German Armed Forces High Command during Nazi Germany, overseeing the strategic direction and coordination of the Wehrmacht in World War II.
  • C. OWF
    OWF is the acronym for the Office of Wildland Fire, a U.S. federal office responsible for coordinating and overseeing wildland fire management policies and programs.
  • D. OSWP
    OSWP (Offensive Security Wireless Professional) is a specialized cybersecurity certification focused on assessing and exploiting vulnerabilities in wireless networks.
  • E. WEOG
    WEOG is the United Nations’ regional group for Western European and other like-minded states, used primarily for consultations and the allocation of seats in UN bodies.
  • 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_69ca834f70e88190b2d74828b7767ec1 completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cdcee04afc8190904704d66e23a432 completed April 2, 2026, 2:05 a.m.
NED1 Entity disambiguation (via context triple) batch_69d28268ab648190a565472d00b289c2 completed April 5, 2026, 3:40 p.m.
NEDg Description generation batch_69d283c516708190b3a1c363841787fc completed April 5, 2026, 3:46 p.m.
NED2 Entity disambiguation (via description) batch_69d284956730819099e5cd918e722fd8 completed April 5, 2026, 3:49 p.m.
Created at: March 30, 2026, 8:55 p.m.