Triple
T29407882
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Executive Tower |
E745817
|
entity |
| Predicate | servesAsWorkplaceFor |
P93741
|
FINISHED |
| Object | President of Uruguay |
—
|
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: President of Uruguay | Statement: [Executive Tower, servesAsWorkplaceFor, President of Uruguay]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: servesAsWorkplaceFor Context triple: [Executive Tower, servesAsWorkplaceFor, President of Uruguay]
-
A.
worksNear
Indicates that one entity performs its work or duties in close physical proximity to another entity.
-
B.
employerIn
Indicates that one entity serves as the employer of another within a specified context, such as a location, organization, or time period.
-
C.
workAt
chosen
Indicates that an entity is employed by or performs work for a particular organization, company, or place.
-
D.
employerService
Indicates that one entity provides employment-related services or functions to another entity, typically in the role of an employer.
-
E.
employerType
Indicates the classification or category of an employer in relation to the entity (e.g., public, private, nonprofit, self-employed).
- 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_69f0a79eb7d081908c67197a5f347e68 |
completed | April 28, 2026, 12:27 p.m. |
| NER | Named-entity recognition | batch_69fdf5d05cc481909ec9e1b1f0784279 |
completed | May 8, 2026, 2:40 p.m. |
| PD | Predicate disambiguation | batch_69fdf0cdd6948190838864ab3120dfa6 |
completed | May 8, 2026, 2:18 p.m. |
Created at: April 28, 2026, 2:55 p.m.