Triple
T9459234
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lilia Skala |
E228097
|
entity |
| Predicate | hasChild |
P369
|
FINISHED |
| Object |
Peter Skala
Peter Skala is the son of Austrian-American actress and architect Lilia Skala.
|
E801375
|
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: Peter Skala | Statement: [Lilia Skala, hasChild, Peter Skala]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Peter Skala Context triple: [Lilia Skala, hasChild, Peter Skala]
-
A.
Victor Slezak
Victor Slezak is an American actor known for his work in film, television, and theater, including roles in dramas such as "The Bridges of Madison County."
-
B.
David Slivka
David Slivka was an American sculptor and painter known for his abstract, expressionist works and contributions to mid-20th-century modern art.
-
C.
Joe Pisarcik
Joe Pisarcik is a former NFL quarterback best known for his infamous late-game fumble in 1978 that led to the "Miracle at the Meadowlands."
-
D.
Martin Straka
Martin Straka is a retired Czech professional ice hockey forward and Olympic gold medalist who enjoyed a long NHL career before becoming a prominent figure in Czech domestic hockey.
-
E.
Jan Triska
Jan Triska was a Czech-American actor known for his intense character roles in film, television, and theater, including notable performances in both European cinema and Hollywood productions.
- 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: Peter Skala Triple: [Lilia Skala, hasChild, Peter Skala]
Generated description
Peter Skala is the son of Austrian-American actress and architect Lilia Skala.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Peter Skala Target entity description: Peter Skala is the son of Austrian-American actress and architect Lilia Skala.
-
A.
Victor Slezak
Victor Slezak is an American actor known for his work in film, television, and theater, including roles in dramas such as "The Bridges of Madison County."
-
B.
David Slivka
David Slivka was an American sculptor and painter known for his abstract, expressionist works and contributions to mid-20th-century modern art.
-
C.
Joe Pisarcik
Joe Pisarcik is a former NFL quarterback best known for his infamous late-game fumble in 1978 that led to the "Miracle at the Meadowlands."
-
D.
Martin Straka
Martin Straka is a retired Czech professional ice hockey forward and Olympic gold medalist who enjoyed a long NHL career before becoming a prominent figure in Czech domestic hockey.
-
E.
Jan Triska
Jan Triska was a Czech-American actor known for his intense character roles in film, television, and theater, including notable performances in both European cinema and Hollywood productions.
- 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_69ca843b123881909b0e60028475d12d |
completed | March 30, 2026, 2:10 p.m. |
| NER | Named-entity recognition | batch_69cd7fc916348190aeb3874a89071677 |
completed | April 1, 2026, 8:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1228d7a488190b537db256f386786 |
completed | April 4, 2026, 2:39 p.m. |
| NEDg | Description generation | batch_69d12395841c8190857de8a50ab6345c |
completed | April 4, 2026, 2:43 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d1275bcbd88190a5742a9cf802425a |
completed | April 4, 2026, 2:59 p.m. |
Created at: March 30, 2026, 7:52 p.m.