Triple
T17136829
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Muzeum station |
E415859
|
entity |
| Predicate | fareZone |
P844
|
FINISHED |
| Object | PID Prague P |
E1192869
|
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: PID Prague P | Statement: [Muzeum station, fareZone, PID Prague P]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: PID Prague P Context triple: [Muzeum station, fareZone, PID Prague P]
-
A.
PID Prague
PID Prague is Prague’s integrated public transport system that unifies metro, trams, buses, and suburban services under a common ticketing and fare structure.
-
B.
PID Prague inner zone
chosen
PID Prague inner zone is the central tariff area of Prague’s integrated public transport system, covering the core city zones for unified ticketing and fares.
-
C.
Prague 3
Prague 3 is a central district of Prague known for its historic neighborhoods, including Žižkov, and notable cultural and religious sites such as the New Jewish Cemetery.
-
D.
42 Prague
42 Prague is a tuition-free, peer-to-peer programming school in the Czech Republic that follows the innovative, project-based learning model of the international 42 network.
-
E.
Prague 1
Prague 1 is the historic central district of Prague, encompassing many of the city’s most famous landmarks, government buildings, and tourist attractions.
- 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_69d886d15af4819092f92f8a129763e6 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3f2cf1c588190986167adcf4851b5 |
completed | April 18, 2026, 9:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a014152da008190bbbff4147cfd8c5b |
completed | May 11, 2026, 2:39 a.m. |
Created at: April 10, 2026, 5:36 a.m.