Triple
T5822682
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Filozofická fakulta Univerzity Karlovy |
E129147
|
entity |
| Predicate | adresa |
P606
|
FINISHED |
| Object | náměstí Jana Palacha 2, 116 38 Praha 1 |
—
|
LITERAL 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: náměstí Jana Palacha 2, 116 38 Praha 1 | Statement: [Filozofická fakulta Univerzity Karlovy, adresa, náměstí Jana Palacha 2, 116 38 Praha 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: adresa Context triple: [Filozofická fakulta Univerzity Karlovy, adresa, náměstí Jana Palacha 2, 116 38 Praha 1]
-
A.
address
Indicates that one entity directs spoken or written communication specifically to another entity.
-
B.
addresses
Indicates that one entity directs speech, communication, or written correspondence specifically toward another entity.
-
C.
addressType
Indicates the specific kind or category of address associated with an entity (e.g., home, work, billing, or shipping).
-
D.
postalAddressFor
Indicates that one entity serves as the postal address associated with, or used for sending mail to, another entity.
-
E.
streetAddress
chosen
Indicates the specific location of an entity in terms of its numbered building and street name within a postal address.
- F. None of above.
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_69c0084869e881908d7859492183ca7b |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0400f1af881908d376ea4793f6dea |
completed | March 22, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_69c0333fdd7081908d829265caa2ac11 |
completed | March 22, 2026, 6:21 p.m. |
Created at: March 22, 2026, 3:53 p.m.