Triple
T24577236
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rally Portugal |
E608147
|
entity |
| Predicate | typicalBaseCity |
P136309
|
FINISHED |
| Object | Porto |
—
|
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: Porto | Statement: [Rally Portugal, typicalBaseCity, Porto]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalBaseCity Context triple: [Rally Portugal, typicalBaseCity, Porto]
-
A.
formerBaseCity
Indicates that a location previously served as the primary base or home city for an entity, but no longer holds that status.
-
B.
typicalVenueCity
Indicates that a particular city is the usual or standard location where an event, activity, or organization is typically held or based.
-
C.
usualCity
chosen
Indicates that a city is the standard, typical, or commonly associated city for a given entity (such as a person, organization, or activity).
-
D.
servesAsFocusCityFor
Indicates that a city functions as a primary or designated focus city for an airline, organization, or transportation network, typically hosting significant but not hub-level operations or activities.
-
E.
typicalCityExample
Indicates that the subject is a representative or characteristic example of a city, illustrating typical features or qualities associated with cities.
- 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_69e2c4cdab6c8190aae6e5d3de55c95e |
completed | April 17, 2026, 11:39 p.m. |
| NER | Named-entity recognition | batch_69f2a97be2ac8190aecf5e54a37e266a |
completed | April 30, 2026, 12:59 a.m. |
| PD | Predicate disambiguation | batch_69f2a6c1f07081908edf0b521767e79b |
completed | April 30, 2026, 12:48 a.m. |
Created at: April 18, 2026, 2:29 a.m.