Triple
T17429503
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Befreiungshalle near Kelheim |
E423829
|
entity |
| Predicate | numberOfVictoryStatues |
P22479
|
FINISHED |
| Object | 34 |
—
|
LITERAL 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: 34 | Statement: [Befreiungshalle near Kelheim, numberOfVictoryStatues, 34]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfVictoryStatues Context triple: [Befreiungshalle near Kelheim, numberOfVictoryStatues, 34]
-
A.
topStatue
Indicates that one entity is positioned as a statue on top of another entity.
-
B.
numberOfSculptures
chosen
Indicates the quantity of sculptures associated with a given entity or context.
-
C.
hasNumberOfMonuments
Indicates the specific count of monuments associated with or present in a given entity.
-
D.
otherMonuments
Indicates that there exists a relationship between an entity and additional monuments that are associated with or related to it in some relevant way.
-
E.
eraOfOriginalMonuments
Indicates the historical time period during which the original monuments were created or first established.
- F. None of above.
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_69d889d88b6081908bada047f5b3ba51 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e448fe9e28819092a80f5686eb362f |
completed | April 19, 2026, 3:16 a.m. |
| PD | Predicate disambiguation | batch_69e3b030eac481909b8402719cc3102e |
completed | April 18, 2026, 4:24 p.m. |
Created at: April 10, 2026, 5:46 a.m.