Triple
T12839614
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paris Métro Gare de Lyon |
E307013
|
entity |
| Predicate | servedByLine |
P1293
|
FINISHED |
| Object | RER D |
E38856
|
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 D | Statement: [Paris Métro Gare de Lyon, servedByLine, RER D]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: RER D Context triple: [Paris Métro Gare de Lyon, servedByLine, RER D]
-
A.
RER D
chosen
RER D is one of the main lines of the Paris regional express network (RER), connecting northern and southern suburbs through central Paris.
-
B.
RER E
RER E is a line of the Paris express suburban rail network (Réseau Express Régional) serving eastern suburbs and connecting them to central Paris.
-
C.
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.
-
D.
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.
-
E.
RER NG
RER NG is a new-generation double-deck electric multiple unit train designed for Île-de-France’s RER network, offering higher capacity, improved accessibility, and enhanced passenger comfort.
- 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_69d7bdf52b94819096d6f0ba4ab50a98 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96ff11b4481909fb2f92c46186853 |
completed | April 10, 2026, 9:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6a54838888190804202e45a55de48 |
completed | May 3, 2026, 1:30 a.m. |
Created at: April 9, 2026, 5:35 p.m.