Triple

T2756571
Position Surface form Disambiguated ID Type / Status
Subject Largo, Maryland E61115 entity
Predicate near P350 FINISHED
Object FedExField E1378 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: FedExField | Statement: [Largo, Maryland, near, FedExField]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: FedExField
Context triple: [Largo, Maryland, near, FedExField]
  • A. FedExField chosen
    FedExField is a large outdoor football stadium in Landover, Maryland, best known as the longtime home venue of Washington’s NFL franchise.
  • B. FedEx
    FedEx is a global courier delivery services company known for its overnight shipping and pioneering real-time package tracking.
  • C. United Parcel Service (UPS)
    United Parcel Service (UPS) is a global package delivery and supply chain management company known for its extensive logistics network and brown delivery trucks.
  • D. DHL
    DHL is the Dag Hammarskjöld Library, the United Nations’ main research and information resource center located at its headquarters in New York.
  • E. DHL
    DHL is a global logistics and courier company known for its international express mail, freight transportation, and supply chain management services.
  • 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_69ab4b7a85bc819094a349b84beb1f2c completed March 6, 2026, 9:47 p.m.
NER Named-entity recognition batch_69abdb8a292c8190ab3982434805241a completed March 7, 2026, 8:02 a.m.
NED1 Entity disambiguation (via context triple) batch_69afbbdf650c8190baa020143b51f94b completed March 10, 2026, 6:36 a.m.
Created at: March 6, 2026, 9:56 p.m.