Triple

T5940115
Position Surface form Disambiguated ID Type / Status
Subject Cape Town International Airport E132144 entity
Predicate connectsTo P845 FINISHED
Object Frankfurt E16481 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: Frankfurt | Statement: [Cape Town International Airport, connectsTo, Frankfurt]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Frankfurt
Context triple: [Cape Town International Airport, connectsTo, Frankfurt]
  • A. Frankfurt am Main chosen
    Frankfurt am Main is a major German financial and transportation hub on the River Main, known for hosting the European Central Bank and one of Europe’s busiest airports.
  • B. Mannheim
    Mannheim is a major city in southwestern Germany, known as an important industrial, commercial, and cultural center at the confluence of the Rhine and Neckar rivers.
  • C. Wiesbaden
    Wiesbaden is a historic spa city in western Germany known for its thermal springs, elegant architecture, and role as a regional administrative and cultural center.
  • D. Cologne
    Cologne is a historic German city on the Rhine River, renowned for its Gothic cathedral, vibrant cultural scene, and status as a major economic and media hub.
  • E. Düsseldorf
    Düsseldorf is a major German city on the Rhine River known for its fashion and art scenes, modern architecture, and status as an important economic and financial center.
  • 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_69c0085c55dc8190aa90e242c956e2fa completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c038f101c081908fb530d2f1f358fc completed March 22, 2026, 6:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6cae2c7f4819096354202532ae488 completed March 27, 2026, 6:22 p.m.
Created at: March 22, 2026, 4:01 p.m.