Triple
T35485329
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Romance in Rio |
E1025579
|
entity |
| Predicate | hasInfluenceFromLocation |
P23136
|
FINISHED |
| Object | Rio de Janeiro |
—
|
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: Rio de Janeiro | Statement: [Romance in Rio, hasInfluenceFromLocation, Rio de Janeiro]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasInfluenceFromLocation Context triple: [Romance in Rio, hasInfluenceFromLocation, Rio de Janeiro]
-
A.
hasRegionalInfluenceFrom
Indicates that one entity’s influence, impact, or authority in a region is derived from or shaped by another entity.
-
B.
hasInfluenceScope
Indicates the range or extent within which an entity’s influence, impact, or authority is effective or applicable.
-
C.
influencesRegion
Indicates that one entity has an effect on, shapes, or alters the conditions, characteristics, or behavior of a specified region.
-
D.
placeOfInfluence
chosen
Indicates the location or area where an entity exerts significant impact, authority, or cultural, social, or intellectual influence.
-
E.
influencedIn
Indicates that one entity had an effect on or shaped another entity within a specific context, domain, or setting.
- 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_69f76dfbcdd881908c7b0b6bc502252b |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_6a006be462288190ae18e1567e2ad29f |
completed | May 10, 2026, 11:28 a.m. |
| PD | Predicate disambiguation | batch_6a0069e7a424819098d38458c3823605 |
completed | May 10, 2026, 11:20 a.m. |
Created at: May 3, 2026, 4:04 p.m.