Triple
T27409363
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vitória de Guimarães SC |
E692098
|
entity |
| Predicate | hasHomeCityFeature |
P101499
|
FINISHED |
| Object | historic city of Guimarães |
—
|
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: historic city of Guimarães | Statement: [Vitória de Guimarães SC, hasHomeCityFeature, historic city of Guimarães]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHomeCityFeature Context triple: [Vitória de Guimarães SC, hasHomeCityFeature, historic city of Guimarães]
-
A.
hasHomeCity
Indicates that an entity’s primary or official city of residence or affiliation is a specified city.
-
B.
hasHomeCityPopulationCharacteristic
Indicates that an entity’s home city possesses a specified population-related attribute or characteristic.
-
C.
hostCityFeature
chosen
Indicates that a particular feature, attribute, or characteristic is associated with or present in a host city.
-
D.
hasUrbanFeature
Indicates that a place or area possesses a specific urban element or infrastructure feature (such as roads, parks, or buildings) as part of its built environment.
-
E.
hasResidenceFeature
Indicates that a residence possesses or is characterized by a specific feature or attribute.
- 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_69ef5205fc808190ad3efc5525b8e6d6 |
completed | April 27, 2026, 12:09 p.m. |
| NER | Named-entity recognition | batch_69f71422adac8190a5ceb32dcf820833 |
completed | May 3, 2026, 9:23 a.m. |
| PD | Predicate disambiguation | batch_69f712764d2c819081b64b27e5de4a13 |
completed | May 3, 2026, 9:16 a.m. |
Created at: April 27, 2026, 12:31 p.m.