Triple

T3328255
Position Surface form Disambiguated ID Type / Status
Subject Saint-Denis E69968 entity
Predicate servedBy P82 FINISHED
Object RER B E10905 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: RER B | Statement: [Saint-Denis, servedBy, RER B]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: RER B
Context triple: [Saint-Denis, servedBy, RER B]
  • A. RER A
    RER A is one of the main lines of the Paris regional express network, carrying large volumes of commuters and travelers between central Paris and its suburbs.
  • B. RER B line chosen
    The RER B line is a major Paris regional express railway line that connects central Paris with key northern and southern suburbs, including Charles de Gaulle Airport.
  • C. RER line E
    RER line E is a Paris regional express railway line connecting central Paris with eastern suburbs such as Gagny.
  • D. RER C
    RER C is a major line of the Paris regional express rail network that connects central Paris with several suburbs and key destinations, including access to Orly Airport.
  • E. RER D
    RER D is one of the main lines of the Paris regional express network (RER), connecting northern and southern suburbs through central Paris.
  • 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_69ad85a1829881908942c14075644d0d completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adb1708b908190bc5c4122623d9b77 completed March 8, 2026, 5:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69b65f07e50c8190be1d760f209c4aea completed March 15, 2026, 7:26 a.m.
Created at: March 8, 2026, 3:12 p.m.