Triple

T18319861
Position Surface form Disambiguated ID Type / Status
Subject Dubai Turf E438843 entity
Predicate sponsor P67 FINISHED
Object DP World NE NERFINISHED

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: DP World | Statement: [Dubai Turf, sponsor, DP World]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: DP World
Context triple: [Dubai Turf, sponsor, DP World]
  • A. DP World chosen
    DP World is a Dubai-based global logistics and port operator that manages ports, economic zones, and supply chain services across numerous countries.
  • B. Hutchison Ports
    Hutchison Ports is a global port operator and logistics company that manages a network of container terminals and related facilities across multiple continents.
  • C. AD Ports Group
    AD Ports Group is a leading United Arab Emirates–based ports, logistics, and maritime services company that develops and operates key trade and industrial hubs across the region.
  • D. Corporación Quiport
    Corporación Quiport is a private consortium responsible for managing and developing airport services and infrastructure in Quito, Ecuador.
  • E. APM Terminals
    APM Terminals is a global port and terminal operating company within the A.P. Moller–Maersk Group, managing a worldwide network of container terminals and related logistics services.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8b916a2d081909e249e4902f6aad9 completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e50aa4d3308190883714e1ef6a1d84 completed April 19, 2026, 5:02 p.m.
Created at: April 10, 2026, 10:36 a.m.