Triple
T12082949
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Prague 6 |
E287726
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object | Prague 7 |
E646477
|
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: Prague 7 | Statement: [Prague 6, borderedBy, Prague 7]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Prague 7 Context triple: [Prague 6, borderedBy, Prague 7]
-
A.
Prague 7
chosen
Prague 7 is a municipal district of Prague, Czech Republic, known for its residential neighborhoods, parks, and cultural institutions along the Vltava River.
-
B.
Prague 8
Prague 8 is a municipal district of Prague that includes a mix of historic neighborhoods and modern residential and commercial areas along the northeastern part of the city.
-
C.
Prague 6
Prague 6 is a large municipal district of Prague, Czech Republic, known for its residential neighborhoods, diplomatic quarter, and proximity to Prague Castle and the airport.
-
D.
Prague 5
Prague 5 is a large municipal district of Prague known for its mix of residential neighborhoods, commercial areas, and green spaces on the western side of the city.
-
E.
Prague 9
Prague 9 is a municipal district of Prague in the Czech Republic, known for its mix of residential areas, industrial zones, and major venues such as large sports and entertainment arenas.
- 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_69d6ab4964708190850585628b287b0c |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d915124e4c8190b0264c2a09e3c2f3 |
completed | April 10, 2026, 3:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f63454d1b88190a01db0e2a7363d05 |
completed | May 2, 2026, 5:28 p.m. |
Created at: April 8, 2026, 9:48 p.m.