Triple
T12437583
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Neuilly-sur-Marne |
E297186
|
entity |
| Predicate | hasTransportConnection |
P845
|
FINISHED |
| Object | RER line A |
E43969
|
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 A | Statement: [Neuilly-sur-Marne, hasTransportConnection, RER line A]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: RER line A Context triple: [Neuilly-sur-Marne, hasTransportConnection, RER line A]
-
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 Line C
RER Line C is a major commuter rail line in the Paris RER network, running along the Seine and serving numerous suburbs and key destinations in the Île-de-France region.
-
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 A
chosen
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.
- 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_69d6ada166c48190b902972cd2408fa3 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94d8c8fd481909b35ac504127a1b6 |
completed | April 10, 2026, 7:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f64b9aaf9c819098e8f8ba40c73363 |
completed | May 2, 2026, 7:08 p.m. |
Created at: April 8, 2026, 9:55 p.m.