Triple
T11236515
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michael Haneke |
E265954
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Haneke |
E265954
|
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: Haneke | Statement: [Michael Haneke, familyName, Haneke]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Haneke Context triple: [Michael Haneke, familyName, Haneke]
-
A.
Michael Haneke
chosen
Michael Haneke is an acclaimed Austrian film director and screenwriter known for his austere, unsettling dramas that critically examine modern society and human psychology.
-
B.
Schygulla
Schygulla is a German surname most famously borne by actress Hanna Schygulla, a prominent figure in New German Cinema.
-
C.
Oliver Hirschbiegel
Oliver Hirschbiegel is a German film and television director best known internationally for his acclaimed World War II drama "Downfall."
-
D.
Cronenbourg
Cronenbourg is a district of Strasbourg, France, known as a residential and industrial area that is integrated into the city’s public transport network.
-
E.
De Munt
De Munt is the Dutch name for the historic Mint Tower, a notable landmark in central Amsterdam.
- 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_69d6aac656d48190b275efaa7d6074ee |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e904cf888190826fc964f76b5cb2 |
completed | April 9, 2026, 5:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e4ad6308f8819085652d6c529ac821 |
completed | April 19, 2026, 10:24 a.m. |
Created at: April 8, 2026, 9:30 p.m.