Triple
T21081779
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wójcicki |
E519386
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Piotr Wójcik
Piotr Wójcik is a notable Polish individual distinguished enough to be recognized as a prominent bearer of the Wójcicki surname.
|
E1492253
|
NE FINISHED |
How this triple was built (4 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: Piotr Wójcik | Statement: [Wójcicki, hasNotableBearer, Piotr Wójcik]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Piotr Wójcik Context triple: [Wójcicki, hasNotableBearer, Piotr Wójcik]
-
A.
Tomasz Wójcik
Tomasz Wójcik is a notable individual bearing the Polish surname Wójcik, recognized enough to be specifically cited among its prominent holders.
-
B.
Piotr Adamczyk
Piotr Adamczyk is a Polish film and television actor best known internationally for his roles in historical dramas and series such as "Karol: A Man Who Became Pope" and "For All Mankind."
-
C.
Jan Wójcik
Jan Wójcik is a Polish individual notable primarily for bearing the surname Wójcicki.
-
D.
Piotr Wolski
Piotr Wolski is a researcher known for co-authoring scientific work with machine learning scientist Marcin Andrychowicz.
-
E.
Piotr Piekarski
Piotr Piekarski is a Polish middle-distance runner who specialized in the 800 metres and competed internationally in the late 20th century.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Piotr Wójcik Triple: [Wójcicki, hasNotableBearer, Piotr Wójcik]
Generated description
Piotr Wójcik is a notable Polish individual distinguished enough to be recognized as a prominent bearer of the Wójcicki surname.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Piotr Wójcik Target entity description: Piotr Wójcik is a notable Polish individual distinguished enough to be recognized as a prominent bearer of the Wójcicki surname.
-
A.
Tomasz Wójcik
Tomasz Wójcik is a notable individual bearing the Polish surname Wójcik, recognized enough to be specifically cited among its prominent holders.
-
B.
Piotr Adamczyk
Piotr Adamczyk is a Polish film and television actor best known internationally for his roles in historical dramas and series such as "Karol: A Man Who Became Pope" and "For All Mankind."
-
C.
Jan Wójcik
Jan Wójcik is a Polish individual notable primarily for bearing the surname Wójcicki.
-
D.
Piotr Wolski
Piotr Wolski is a researcher known for co-authoring scientific work with machine learning scientist Marcin Andrychowicz.
-
E.
Piotr Piekarski
Piotr Piekarski is a Polish middle-distance runner who specialized in the 800 metres and competed internationally in the late 20th century.
- F. None of above. chosen
Provenance (5 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_69e0b506e59c8190849b71ed07929215 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e702db430c81908a1547d8fbe45506 |
completed | April 21, 2026, 4:53 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a09fd1bb8c48190a2f9dff6a0c2eaeb |
completed | May 17, 2026, 5:38 p.m. |
| NEDg | Description generation | batch_6a09fdf8a3f08190b3e1cf046920584d |
completed | May 17, 2026, 5:42 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a09fe7982748190ab3f61e7c2dc32ae |
completed | May 17, 2026, 5:44 p.m. |
Created at: April 16, 2026, 2:49 p.m.