Triple

T675689
Position Surface form Disambiguated ID Type / Status
Subject RER C E13072 entity
Predicate serves P98 FINISHED
Object Orly Airport E2174 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: Orly Airport | Statement: [RER C, serves, Orly Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Orly Airport
Context triple: [RER C, serves, Orly Airport]
  • A. Orly Airport chosen
    Orly Airport is a major international airport serving Paris, France, located south of the city and handling a large share of its domestic and European flights.
  • B. Ben-Gurion Airport
    Ben-Gurion Airport is Israel’s main international airport, located near Tel Aviv and serving as the country’s primary gateway for global air travel.
  • C. Haifa Airport
    Haifa Airport is a small international airport in northern Israel serving domestic flights and limited regional routes for the city of Haifa.
  • D. Damascus International Airport
    Damascus International Airport is the main international gateway serving Syria’s capital, handling the majority of the country’s international air traffic.
  • E. Beirut–Rafic Hariri International Airport
    Beirut–Rafic Hariri International Airport is Lebanon’s main international gateway, serving as the primary airport for Beirut and the country’s largest and busiest aviation hub.
  • 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_69a4933d3bf88190972041cd8cf143b9 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a4a0266e7c8190a94c4b4b761c59f4 completed March 1, 2026, 8:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69a5c3a1b6588190b0c9215afb3a9200 completed March 2, 2026, 5:06 p.m.
Created at: March 1, 2026, 7:36 p.m.