Triple
T21830177
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Svatava Pírková-Jakobson |
E538972
|
entity |
| Predicate | languagesWorkedWith |
P45484
|
FINISHED |
| Object | Czech |
—
|
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: Czech | Statement: [Svatava Pírková-Jakobson, languagesWorkedWith, Czech]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languagesWorkedWith Context triple: [Svatava Pírková-Jakobson, languagesWorkedWith, Czech]
-
A.
languagesUsed
Indicates that one entity uses, employs, or is expressed in one or more languages associated with the other entity.
-
B.
workedWithLanguageIndustry
Indicates that an entity has performed work or professional activities within the language industry, such as translation, localization, interpreting, or related language services.
-
C.
hasWorkedInLanguage
chosen
Indicates that an entity has performed work or professional activities using a particular language.
-
D.
languageOfPrimaryProgramming
Indicates the programming language that is primarily used to implement or develop a given entity.
-
E.
programLanguage
Indicates that an entity is implemented, written, or expressed using a particular programming language.
- 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_69e0c475cda88190987d08f23caebdc1 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f0913554508190b81347f01d3903e8 |
completed | April 28, 2026, 10:51 a.m. |
| PD | Predicate disambiguation | batch_69e6be815a108190be81d7c987d0c0d6 |
completed | April 21, 2026, 12:02 a.m. |
Created at: April 16, 2026, 6:54 p.m.