Triple
T12090262
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Into the Badlands |
E287922
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Marton Csokas |
E205097
|
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: Marton Csokas | Statement: [Into the Badlands, starring, Marton Csokas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marton Csokas Context triple: [Into the Badlands, starring, Marton Csokas]
-
A.
Marton Csokas
chosen
Marton Csokas is a New Zealand actor known for his versatile character roles in international films and television series, including major action and fantasy franchises.
-
B.
András Nagy
András Nagy is a Hungarian biologist and stem cell researcher known for his pioneering work in embryonic stem cells and regenerative medicine.
-
C.
Zoltán Huszárik
Zoltán Huszárik was a Hungarian film director and visual artist renowned for his poetic, experimental style and influential works in 20th-century Hungarian cinema.
-
D.
Gábor Nagy
Gábor Nagy is a Hungarian given name borne by several notable individuals across fields such as politics, sports, and academia.
-
E.
Zoltán Nagy
Zoltán Nagy is a Hungarian name shared by several notable individuals, including professionals in fields such as sports, music, and academia.
- 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_69d6ab4964708190850585628b287b0c |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d915161f848190a6355c1e372eadaa |
completed | April 10, 2026, 3:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f61e42cd588190835b3e8160bdbba5 |
completed | May 2, 2026, 3:54 p.m. |
Created at: April 8, 2026, 9:48 p.m.