Triple
T20917184
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ftan |
E515101
|
entity |
| Predicate | neighboringMunicipality |
P17964
|
FINISHED |
| Object | Sent |
—
|
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: Sent | Statement: [Ftan, neighboringMunicipality, Sent]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sent Context triple: [Ftan, neighboringMunicipality, Sent]
-
A.
Sent
chosen
Sent is a picturesque village in the Lower Engadine region of the Swiss canton of Graubünden, known for its traditional architecture and alpine setting.
-
B.
Sendim
Sendim is a town in northeastern Portugal known for its Mirandese-speaking community and distinct local culture.
-
C.
Envoyer
Envoyer is a zero-downtime deployment service created by the Laravel team for seamlessly deploying PHP and Laravel applications.
-
D.
Senden
Senden is a town in the district of Neu-Ulm in Bavaria, Germany, known for its residential character and proximity to the cities of Ulm and Neu-Ulm.
-
E.
Senden
Senden is a municipality in western Germany known for its rural character and proximity to the city of Münster in the state of North Rhine-Westphalia.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4f9d5ec8190bb2bd27350ed341c |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6ec635f4881909a560fb891100d8c |
completed | April 21, 2026, 3:17 a.m. |
Created at: April 16, 2026, 12:48 p.m.