Triple
T6553939
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Goussainville |
E152397
|
entity |
| Predicate | railService |
P522
|
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: [Goussainville, railService, RER D]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: RER D Context triple: [Goussainville, railService, 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 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.
-
C.
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.
-
D.
RER line D
RER line D is one of the main lines of the Paris RER suburban rail network, running north–south through central Paris and serving numerous suburbs across the Île-de-France region.
-
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_69c688058d6881908c19b309cc55dbfa |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6ae0847d88190b38f9d7dba0faae1 |
completed | March 27, 2026, 4:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6d55707d081908104f08e1d59d603 |
completed | March 27, 2026, 7:07 p.m. |
Created at: March 27, 2026, 1:51 p.m.