Triple
T35609108
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Murten/Morat railway station |
E1028983
|
entity |
| Predicate | locatedInBilingualTown |
P21622
|
FINISHED |
| Object | Murten (Morat) |
E315007
|
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: Murten (Morat) | Statement: [Murten/Morat railway station, locatedInBilingualTown, Murten (Morat)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: locatedInBilingualTown Context triple: [Murten/Morat railway station, locatedInBilingualTown, Murten (Morat)]
-
A.
isBilingual
Indicates that an entity is able to communicate fluently in two distinct languages.
-
B.
isBilingualRegion
chosen
Indicates that a region officially uses two languages or has two predominant languages in regular use.
-
C.
hasSecondaryLanguageNearby
Indicates that an entity has at least one secondary language present or used in its immediate vicinity or surrounding context.
-
D.
hasNeighboringLanguageCommunity
Indicates that one language community is geographically or socially adjacent to another, allowing for direct contact or interaction between them.
-
E.
spokenInNeighboringRegionsOf
Indicates that a language or speech variety is used in regions that geographically border the primary region associated with another language or entity.
- F. None of above.
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_69f76e0653ec81909b1b813c126c6574 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_6a037c8d06cc8190ab6a5e18d9d2571e |
completed | May 12, 2026, 7:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a390d2b791c8190a30392adb5fd3335 |
completed | June 22, 2026, 10:23 a.m. |
| PD | Predicate disambiguation | batch_6a037a04d8348190a4819666eab42c9b |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:05 p.m.