Triple
T16199787
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Prague commuter rail |
E393166
|
entity |
| Predicate | connectsTo |
P845
|
FINISHED |
| Object | Benešov u Prahy |
E227473
|
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: Benešov u Prahy | Statement: [Prague commuter rail, connectsTo, Benešov u Prahy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Benešov u Prahy Context triple: [Prague commuter rail, connectsTo, Benešov u Prahy]
-
A.
Benešov
chosen
Benešov is a town in the Czech Republic known as a local administrative and cultural center southeast of Prague.
-
B.
Bubeneč
Bubeneč is a residential and diplomatic district in Prague known for its embassies, green spaces, and proximity to Stromovka park.
-
C.
Bečej
Bečej is a town and municipality in northern Serbia, situated in the autonomous province of Vojvodina.
-
D.
Nymburk
Nymburk is a historic town in the Czech Republic known for its medieval fortifications and location on the Elbe River.
-
E.
Benešov main square
Benešov main square is the central historic plaza of the Czech town of Benešov, serving as its main social, commercial, and cultural gathering place.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d87f1f5bd08190bd01cac0d5b9d2ef |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e222de2db481908471b9c73d444607 |
completed | April 17, 2026, 12:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffff1107908190afda091b53317d81 |
completed | May 10, 2026, 3:44 a.m. |
Created at: April 10, 2026, 5:03 a.m.