Triple
T13686812
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Antoni Ponikowski |
E328150
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Antoni Ponikowski |
E328150
|
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: Antoni Ponikowski | Statement: [Antoni Ponikowski, name, Antoni Ponikowski]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Antoni Ponikowski Context triple: [Antoni Ponikowski, name, Antoni Ponikowski]
-
A.
Antoni Ponikowski
chosen
Antoni Ponikowski was a Polish politician and academic who twice served as prime minister during the turbulent years surrounding Poland’s regaining of independence after World War I.
-
B.
Tomasz Dąbrowski
Tomasz Dąbrowski is a Polish jazz trumpeter and composer known for his work in contemporary and avant-garde jazz.
-
C.
Tomasz Arciszewski
Tomasz Arciszewski was a Polish socialist politician and statesman who served as prime minister of the Polish government-in-exile during World War II.
-
D.
Filip Wolski
Filip Wolski is a machine learning researcher known for his work at OpenAI, including contributions to reinforcement learning methods such as Proximal Policy Optimization (PPO).
-
E.
Pawel Pogorzelski
Pawel Pogorzelski is a cinematographer known for his visually striking, atmospheric work on films such as Ari Aster’s horror feature "Midsommar."
- 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_69d8076f1fa8819094664a59b55010df |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbc670968881908e2b4fdf656c7285 |
completed | April 12, 2026, 4:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7944981ec8190be5ff39b7c2c70ab |
completed | May 3, 2026, 6:30 p.m. |
Created at: April 9, 2026, 9:53 p.m.