Triple
T3509743
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Henriette Pressburg |
E74165
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Pressburg |
E126688
|
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: Pressburg | Statement: [Henriette Pressburg, familyName, Pressburg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pressburg Context triple: [Henriette Pressburg, familyName, Pressburg]
-
A.
Pozsony
chosen
Pozsony is the historical Hungarian name for the city now known as Bratislava, the capital of Slovakia.
-
B.
Visegrád
Visegrád is a historic town in northern Hungary on the Danube River, renowned for its medieval castle and royal palace that once served as a seat of Hungarian kings.
-
C.
Troppau
Troppau is a historic Central European town, now known as Opava in the Czech Republic, that has long served as an important regional administrative and cultural center in Silesia.
-
D.
Kežmarok
Kežmarok is a historic town in northern Slovakia known for its well-preserved medieval architecture and role as a cultural center of the Spiš (Spisz) region.
-
E.
Vienna
Vienna is a small town in Dane County, Wisconsin, known for its rural character and proximity to the Madison metropolitan area.
- 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_69ad85ce7a9c81909ddc5cf0cb67a6e3 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbc0e1f0c8190b054d9fba16ce4b3 |
completed | March 8, 2026, 6:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b3bb809d288190b4904b292fe9a627 |
completed | March 13, 2026, 7:23 a.m. |
Created at: March 8, 2026, 3:18 p.m.