Triple
T1720332
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Villeneuve-le-Roi |
E37374
|
entity |
| Predicate | hasTransportConnection |
P845
|
FINISHED |
| Object | RER line C |
E13072
|
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 line C | Statement: [Villeneuve-le-Roi, hasTransportConnection, RER line C]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: RER line C Context triple: [Villeneuve-le-Roi, hasTransportConnection, RER line C]
-
A.
RER line E
RER line E is a Paris regional express railway line connecting central Paris with eastern suburbs such as Gagny.
-
B.
RER B line
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 C
chosen
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 network
The RER network is a rapid transit system of express suburban trains serving Paris and its surrounding metropolitan area, integrating both urban and regional rail 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_69a8861912dc8190931af43b4b9158a7 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aa63558d7c8190830cb8ee2e4a8932 |
completed | March 6, 2026, 5:17 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad8ae98cd88190af4dc46679b3d93f |
completed | March 8, 2026, 2:42 p.m. |
Created at: March 4, 2026, 7:30 p.m.