Triple
T3890441
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paris–Strasbourg |
E88047
|
entity |
| Predicate | connectsToInternationalNetwork |
P11099
|
FINISHED |
| Object | German rail network |
—
|
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: German rail network | Statement: [Paris–Strasbourg, connectsToInternationalNetwork, German rail network]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: connectsToInternationalNetwork Context triple: [Paris–Strasbourg, connectsToInternationalNetwork, German rail network]
-
A.
isMajorInternationalGatewayFor
Indicates that one entity serves as a primary, globally significant access point or hub for another entity’s international connections or flows.
-
B.
isInternational
Indicates that something has a connection to, involves, or extends across more than one country.
-
C.
hasInternationalService
chosen
Indicates that an entity provides or is connected to transportation or communication services that operate across national borders.
-
D.
operatesInternationally
Indicates that the entity conducts activities or business across national borders in multiple countries.
-
E.
hasNetworkUS
Indicates that an entity possesses, operates, or is associated with a network located in or serving the United States.
- 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_69aed9466d548190939f5217a23ed4ac |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeecb0ba448190aa076865b7762002 |
completed | March 9, 2026, 3:52 p.m. |
| PD | Predicate disambiguation | batch_69aee759609c8190985e96ec6d96dedd |
completed | March 9, 2026, 3:29 p.m. |
Created at: March 9, 2026, 3:21 p.m.