Triple
T18058371
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mora |
E432099
|
entity |
| Predicate | nearbySettlement |
P350
|
FINISHED |
| Object | Orsa |
—
|
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: Orsa | Statement: [Mora, nearbySettlement, Orsa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Orsa Context triple: [Mora, nearbySettlement, Orsa]
-
A.
Orsa
chosen
Orsa is a small locality and municipality in central Sweden known for its forests, lakes, and traditional Dalarna culture.
-
B.
Oros
Oros is a surname most notably associated with Joe Oros, an American automobile designer known for his work at Ford.
-
C.
Oros
Oros is a town in Maharashtra, India, that serves as an administrative and commercial center for the surrounding Sindhudurg district.
-
D.
Keila
Keila is a small town in northern Estonia known for its historic church, scenic Keila River and waterfall, and role as a local administrative and transport hub.
-
E.
Orosháza
Orosháza is a town in southeastern Hungary known for its agricultural economy, thermal baths, and proximity to the Great Hungarian Plain.
- 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_69d8b906482481908183315b9ecf9994 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4c1048c00819097c7dfbf76bb0987 |
completed | April 19, 2026, 11:48 a.m. |
Created at: April 10, 2026, 10:26 a.m.