Triple
T33529292
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Club Nacional |
E858727
|
entity |
| Predicate | hasRivalryIn |
P187405
|
FINISHED |
| Object | Paraguayan football |
—
|
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: Paraguayan football | Statement: [Club Nacional, hasRivalryIn, Paraguayan football]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRivalryIn Context triple: [Club Nacional, hasRivalryIn, Paraguayan football]
-
A.
hasRivalryAspect
Indicates that there exists a competitive or adversarial relationship or dimension between entities.
-
B.
hasRivalryRoot
Indicates that a rivalry relationship originates from, or is fundamentally based on, a particular source, cause, or underlying factor.
-
C.
hasRivalryContext
Indicates that there exists a competitive or adversarial relationship between entities within a specific situational or contextual framework.
-
D.
hasLocalRivalry
Indicates that there is an ongoing competitive or adversarial relationship between entities that are geographically close or share the same local area.
-
E.
associatedRivalry
Indicates a relationship where one entity is linked to another as its rival, competitor, or opposing counterpart.
- F. None of above. chosen
Provenance (4 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_69f34978caf4819083f90eba4944d8e8 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69fb563aec448190875410fb1a3ed624 |
completed | May 6, 2026, 2:54 p.m. |
| PD | Predicate disambiguation | batch_69fb35b9ede881908aaae93a215525df |
completed | May 6, 2026, 12:36 p.m. |
| PDg | Predicate description generation | batch_69fb563a28d88190b28345c465c545f8 |
completed | May 6, 2026, 2:54 p.m. |
Created at: May 1, 2026, 1:39 a.m.