Triple
T9861732
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zaleski |
E239729
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Jan Zaleski
Jan Zaleski was a Polish biochemist known for his pioneering research in organic and physiological chemistry in the early 20th century.
|
E848733
|
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: Jan Zaleski | Statement: [Zaleski, hasNotableBearer, Jan Zaleski]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jan Zaleski Context triple: [Zaleski, hasNotableBearer, Jan Zaleski]
-
A.
Antoni Zaleski
Antoni Zaleski is a personal name that may refer to one of several individuals, rather than a single widely recognized public figure.
-
B.
August Zaleski
August Zaleski was a Polish diplomat and politician who served as President of Poland in exile after World War II.
-
C.
Jacek Malczewski
Jacek Malczewski was a prominent Polish painter associated with Symbolism, known for his allegorical and patriotic works at the turn of the 19th and 20th centuries.
-
D.
Ignacy Witczak
Ignacy Witczak was a Soviet intelligence officer who operated undercover as a diplomat in the United States during World War II.
-
E.
Janusz Laskowski
Janusz Laskowski is a Polish professional associated with Wrocław University of Science and Technology, recognized as one of its notable alumni.
- 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: Jan Zaleski Triple: [Zaleski, hasNotableBearer, Jan Zaleski]
Generated description
Jan Zaleski was a Polish biochemist known for his pioneering research in organic and physiological chemistry in the early 20th century.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jan Zaleski Target entity description: Jan Zaleski was a Polish biochemist known for his pioneering research in organic and physiological chemistry in the early 20th century.
-
A.
Antoni Zaleski
Antoni Zaleski is a personal name that may refer to one of several individuals, rather than a single widely recognized public figure.
-
B.
August Zaleski
August Zaleski was a Polish diplomat and politician who served as President of Poland in exile after World War II.
-
C.
Jacek Malczewski
Jacek Malczewski was a prominent Polish painter associated with Symbolism, known for his allegorical and patriotic works at the turn of the 19th and 20th centuries.
-
D.
Ignacy Witczak
Ignacy Witczak was a Soviet intelligence officer who operated undercover as a diplomat in the United States during World War II.
-
E.
Janusz Laskowski
Janusz Laskowski is a Polish professional associated with Wrocław University of Science and Technology, recognized as one of its notable alumni.
- 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_69ca84e6493081909cf58c8d42ea856b |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb3b6aa108190978f1c0cdc0f45a0 |
completed | April 2, 2026, 12:09 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d380c81c7c81908361d237d79f1ff0 |
completed | April 6, 2026, 9:45 a.m. |
| NEDg | Description generation | batch_69d3aa1f726081908c9d10b6d8e72cf0 |
completed | April 6, 2026, 12:42 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d3aaca10c48190aab14dba027190ae |
completed | April 6, 2026, 12:44 p.m. |
Created at: March 30, 2026, 8:35 p.m.