Triple
T4161062
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Georg Büchner Prize |
E91534
|
entity |
| Predicate | hasAwarded |
P2391
|
FINISHED |
| Object | Terézia Mora |
E55110
|
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: Terézia Mora | Statement: [Georg Büchner Prize, hasAwarded, Terézia Mora]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Terézia Mora Context triple: [Georg Büchner Prize, hasAwarded, Terézia Mora]
-
A.
Terézia Mora
chosen
Terézia Mora is a Hungarian-born German writer and translator acclaimed for her innovative prose and contributions to contemporary German-language literature.
-
B.
Marta Kubišová
Marta Kubišová is a Czech singer and dissident renowned both for her powerful anti-communist protest songs and for her prominent role in the Czechoslovak human rights movement.
-
C.
Milena Králíčková
Milena Králíčková is a Czech academic and physician who serves as the rector of Charles University in Prague.
-
D.
Zora Vesecká
Zora Vesecká is a Czech individual whose given name is Zora, a common female name in Slavic countries.
-
E.
Alice Masaryková
Alice Masaryková was a Czech sociologist, politician, and humanitarian, notable as the daughter of Czechoslovakia’s first president and a leading figure in the country’s early social welfare and Red Cross movements.
- 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_69aed9626ebc8190a39de631788bea3e |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af02a6b5f48190bdabf988d23f6e97 |
completed | March 9, 2026, 5:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b59611f8548190a47638bcd95e1541 |
completed | March 14, 2026, 5:08 p.m. |
Created at: March 9, 2026, 3:44 p.m.