Triple
T3812005
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SNCF Réseau |
E93156
|
entity |
| Predicate | hasHighSpeedLines |
P48478
|
FINISHED |
| Object | LGV network in France |
—
|
LITERAL 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: LGV network in France | Statement: [SNCF Réseau, hasHighSpeedLines, LGV network in France]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHighSpeedLines Context triple: [SNCF Réseau, hasHighSpeedLines, LGV network in France]
-
A.
hasHighSpeedLine
chosen
Indicates that there exists a high-speed rail line connection between the related entities.
-
B.
hasHighSpeedRailStation
Indicates that a location is served by a high-speed rail station where high-speed trains regularly stop.
-
C.
hasRailSystem
Indicates that an entity possesses or is served by a rail-based transportation system.
-
D.
hasExpressTracks
Indicates that a transportation route or facility includes tracks designated for express service, allowing faster travel with fewer stops than regular tracks.
-
E.
hasShuttleLine
Indicates that there is a shuttle service or route operating between the related entities.
- 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_69aed96a60088190ab1df8390fffc935 |
completed | March 9, 2026, 2:30 p.m. |
| NER | Named-entity recognition | batch_69aef1515c688190a38332aedeed8a76 |
completed | March 9, 2026, 4:12 p.m. |
| PD | Predicate disambiguation | batch_69aee7482d708190a3ec74745b102a4c |
completed | March 9, 2026, 3:29 p.m. |
Created at: March 9, 2026, 3:16 p.m.