Triple
T16241069
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Petr Pavel |
E394246
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Petr |
E159035
|
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: Petr | Statement: [Petr Pavel, givenName, Petr]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Petr Context triple: [Petr Pavel, givenName, Petr]
-
A.
Petr
chosen
Petr is a common Slavic given name, equivalent to Peter in English.
-
B.
Pavel
Pavel is a Slavic given name, equivalent to the English name Paul.
-
C.
Vaslav
Vaslav is the given name of Vaslav Nijinsky, the legendary early 20th-century ballet dancer and choreographer renowned for his groundbreaking work with the Ballets Russes.
-
D.
Timotej
Timotej is a masculine given name, common in Slavic countries, that is equivalent to Timothy.
-
E.
Petry
Petry is a surname most notably associated with Ann Petry, an influential American novelist and short story writer known for exploring African American life and social issues.
- 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_69d87f2171208190951025e526947816 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e2455e1ce08190b97e2ab3e8c6d535 |
completed | April 17, 2026, 2:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a000edd1ed08190aa0211c3b17a918e |
completed | May 10, 2026, 4:51 a.m. |
Created at: April 10, 2026, 5:04 a.m.