Triple
T31113122
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Waldhof Mannheim |
E793008
|
entity |
| Predicate | hasRivalriesRegion |
P80695
|
FINISHED |
| Object | southwestern Germany |
—
|
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: southwestern Germany | Statement: [Waldhof Mannheim, hasRivalriesRegion, southwestern Germany]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRivalriesRegion Context triple: [Waldhof Mannheim, hasRivalriesRegion, southwestern Germany]
-
A.
rivalryRegion
chosen
Indicates a competitive or adversarial relationship that exists between entities within a specific geographic or regional context.
-
B.
hasLocalRivalry
Indicates that there is an ongoing competitive or adversarial relationship between entities that are geographically close or share the same local area.
-
C.
hasInStateRivalries
Indicates that two entities are rivals or competitors within the same state or internal jurisdiction.
-
D.
hasRivalryCategory
Indicates that there exists a competitive or adversarial relationship between entities that falls into a specific classified type or category of rivalry.
-
E.
hasRivalryRoot
Indicates that a rivalry relationship originates from, or is fundamentally based on, a particular source, cause, or underlying factor.
- 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_69f224cfd5d881908ec6447bc321cd58 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f7764ab1fc81909f9348db87bd7692 |
completed | May 3, 2026, 4:22 p.m. |
| PD | Predicate disambiguation | batch_69f76905d9c88190b1ee810bc9ab644f |
completed | May 3, 2026, 3:25 p.m. |
Created at: April 29, 2026, 9:04 p.m.