Triple
T17591267
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zentralbahn |
E428452
|
entity |
| Predicate | operatesInCanton |
P128128
|
FINISHED |
| Object | Canton of Lucerne |
—
|
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: Canton of Lucerne | Statement: [Zentralbahn, operatesInCanton, Canton of Lucerne]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: operatesInCanton Context triple: [Zentralbahn, operatesInCanton, Canton of Lucerne]
-
A.
governingCantonCapital
Indicates that a city serves as the capital and administrative seat of the specified canton.
-
B.
numberOfCantons
Indicates the total count of cantons associated with or contained within a given entity.
-
C.
foundingCantons
Indicates that the referenced entities are the original member regions or states that established or founded a larger political or organizational unit.
-
D.
hasCanton
Indicates that an entity is administratively divided into, or associated with, a specific canton.
-
E.
officialNameOfCanton
Indicates that one entity is the official, legally recognized name assigned to a particular canton.
- F. None of above. chosen
Provenance (4 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_69d889e1030481909950e140c63255b9 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e469e6e3888190b73a5b6d7e8c0a55 |
completed | April 19, 2026, 5:36 a.m. |
| PD | Predicate disambiguation | batch_69e3b4fff0348190b899a32da537eaca |
completed | April 18, 2026, 4:44 p.m. |
| PDg | Predicate description generation | batch_69e3bbb50b448190a59dd4be33c76db7 |
completed | April 18, 2026, 5:13 p.m. |
Created at: April 10, 2026, 5:51 a.m.