Triple
T30605728
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Porto Corsa |
E779037
|
entity |
| Predicate | countryModeledOn |
P200470
|
FINISHED |
| Object | Italy |
—
|
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: Italy | Statement: [Porto Corsa, countryModeledOn, Italy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: countryModeledOn Context triple: [Porto Corsa, countryModeledOn, Italy]
-
A.
modeledInCountry
Indicates that something (such as a model, design, or representation) was created, developed, or constructed within the specified country.
-
B.
foundingModelledOn
Indicates that the founding or establishment of one entity is based on, inspired by, or patterned after the founding model or principles of another entity.
-
C.
isModelledAfter
Indicates that one entity is created, designed, or structured based on the form, features, or principles of another entity.
-
D.
nicknameModelledOn
Indicates that one entity’s nickname is based on, inspired by, or patterned after another entity.
-
E.
setInCityModelledOn
Indicates that a fictional or constructed city is based on, inspired by, or patterned after a real-world city.
- 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_69f224a21fc08190abd9d8dd9eb6bb4c |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69ff8cecbf048190860b9f72b8753f5c |
completed | May 9, 2026, 7:37 p.m. |
| PD | Predicate disambiguation | batch_69ff8c4c39dc8190b5bf35adc1bae7c6 |
completed | May 9, 2026, 7:34 p.m. |
| PDg | Predicate description generation | batch_69ff8cec1e4c8190b2d66b3e0f913bfd |
completed | May 9, 2026, 7:37 p.m. |
Created at: April 29, 2026, 8:25 p.m.