Triple
T23048098
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Agárd railway stop |
E573932
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Lake Velence |
—
|
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: Lake Velence | Statement: [Agárd railway stop, locatedNear, Lake Velence]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lake Velence Context triple: [Agárd railway stop, locatedNear, Lake Velence]
-
A.
Lake Velence
chosen
Lake Velence is one of Hungary’s largest natural lakes, known as a popular resort and recreation area in the Transdanubian region.
-
B.
Lake Van
Lake Van is the largest lake in Turkey, a saline endorheic lake renowned for its high altitude, unique ecosystem, and historical Armenian cultural sites along its shores.
-
C.
Lake Turano
Lake Turano is an artificial lake in the Lazio region of central Italy, known for its scenic mountain setting and medieval villages along its shores.
-
D.
Vlasina Lake
Vlasina Lake is a high-altitude artificial lake in southeastern Serbia, known for its floating peat islands and surrounding unspoiled nature.
-
E.
Lake Potanipo
Lake Potanipo is a small recreational lake in Brookline, New Hampshire, known for activities like boating, fishing, and swimming.
- 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_69e245b9c11481909d06c872214d21af |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1867a206c81909ff923cbf56f7787 |
completed | April 29, 2026, 4:18 a.m. |
Created at: April 17, 2026, 3:54 p.m.