Triple
T35662774
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saint-Just station |
E1030480
|
entity |
| Predicate | isTerminalStopOn |
P124814
|
FINISHED |
| Object | Lyon’s historic funicular network |
—
|
NE NERFINISHED |
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: Lyon’s historic funicular network | Statement: [Saint-Just station, isTerminalStopOn, Lyon’s historic funicular network]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isTerminalStopOn Context triple: [Saint-Just station, isTerminalStopOn, Lyon’s historic funicular network]
-
A.
isTerminal
Indicates that an entity represents an endpoint or final state in a process, structure, or sequence, with no further continuation beyond it.
-
B.
isTerminalIn
chosen
Indicates that one entity functions as a terminal (end point or final element) within another entity, such as a structure, sequence, or process.
-
C.
hasStopType
Indicates that a stop or stopping point is classified as having a particular type or category of stop.
-
D.
hasStop
Indicates that something (such as a route, service, or journey) includes or is associated with a particular stop or stopping point.
-
E.
terminatesFor
Indicates that one entity causes or marks the ending or cessation of another entity, process, or state.
- F. None of above.
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_69f76e09f87881909c954aaac176c34f |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f79fa913c48190a609dfd9c184afbc |
completed | May 3, 2026, 7:19 p.m. |
| PD | Predicate disambiguation | batch_69f79e4d885881908a3612e2e75cf84f |
completed | May 3, 2026, 7:13 p.m. |
Created at: May 3, 2026, 4:05 p.m.