Triple
T24351242
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nationaltheater Mannheim |
E613793
|
entity |
| Predicate | hasSubsidyFrom |
P8753
|
FINISHED |
| Object | City of Mannheim |
—
|
NE NERFINISHED |
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: City of Mannheim | Statement: [Nationaltheater Mannheim, hasSubsidyFrom, City of Mannheim]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSubsidyFrom Context triple: [Nationaltheater Mannheim, hasSubsidyFrom, City of Mannheim]
-
A.
typeOfSubsidy
Indicates that one entity is a specific kind or category of subsidy in relation to another entity.
-
B.
subsidyAttachment
Indicates that a subsidy is linked or applied to a particular entity, object, or transaction.
-
C.
wasStateSubsidized
Indicates that an entity received financial support or subsidies from a state or government authority.
-
D.
hasLowIncomeSubsidyProgram
Indicates that an entity operates or offers a program providing financial assistance or subsidies specifically targeted at individuals or groups with low income.
-
E.
hasMonetaryGrant
chosen
Indicates that an entity provides or receives a monetary grant from another entity.
- 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_69e2d7ddd29481909e7f539a6072bd71 |
completed | April 18, 2026, 1:01 a.m. |
| NER | Named-entity recognition | batch_69f293457120819098af138fdd01846d |
completed | April 29, 2026, 11:24 p.m. |
| PD | Predicate disambiguation | batch_69f287bb1b2c81909c2e7fcc392ad143 |
completed | April 29, 2026, 10:35 p.m. |
Created at: April 18, 2026, 1:59 a.m.