Triple
T7530931
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Henryk Sienkiewicz |
E178020
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Henryk |
E298594
|
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: Henryk | Statement: [Henryk Sienkiewicz, givenName, Henryk]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Henryk Context triple: [Henryk Sienkiewicz, givenName, Henryk]
-
A.
Henryk
chosen
Henryk is a masculine given name of Germanic origin commonly used in Poland and other Central and Eastern European countries.
-
B.
Ludwik
Ludwik is a given name, primarily used in Polish, that corresponds to the name Ludwig in other European languages.
-
C.
Wiktor
Wiktor is a masculine given name, primarily used in Slavic countries, that corresponds to the name Victor.
-
D.
Tadeusz
Tadeusz is a masculine given name of Slavic origin, particularly common in Poland.
-
E.
Feliks
Feliks is a given name, commonly used in Slavic and other European languages, that corresponds to the name Felix.
- 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_69c69f2acdbc8190b5a8320168c1d0ba |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f8217b1c8190b3db453cee0fc4fd |
completed | March 27, 2026, 9:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8463e08ac8190abd4d19b58067233 |
completed | March 28, 2026, 9:21 p.m. |
Created at: March 27, 2026, 3:47 p.m.