Triple
T28452953
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Acoyte station |
E716627
|
entity |
| Predicate | countryCapitalSystem |
P196694
|
FINISHED |
| Object | Buenos Aires public transport network |
—
|
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: Buenos Aires public transport network | Statement: [Acoyte station, countryCapitalSystem, Buenos Aires public transport network]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: countryCapitalSystem Context triple: [Acoyte station, countryCapitalSystem, Buenos Aires public transport network]
-
A.
countryCapitalOfSeat
Indicates that a country serves as the capital location or official seat of a specified entity, such as an organization or institution.
-
B.
countryCapitalRelation
Indicates that one entity is the capital city of the country represented by the other entity.
-
C.
countryCapitalAssociation
Indicates the relationship in which a specific city serves as the capital of a particular country.
-
D.
countryCapitalContext
Indicates that one entity serves as the capital city of the specified country in a given contextual or temporal setting.
-
E.
destinationCapitalOf
Indicates that a location serves as the capital city of the specified destination or region.
- 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_69efd6b76f8c8190a7ba908aca280942 |
completed | April 27, 2026, 9:35 p.m. |
| NER | Named-entity recognition | batch_69fe629b4fa481908467c7c41b77f0c6 |
completed | May 8, 2026, 10:24 p.m. |
| PD | Predicate disambiguation | batch_69fe61bb260c819083f9378a3a06ca47 |
completed | May 8, 2026, 10:20 p.m. |
| PDg | Predicate description generation | batch_69fe629a8d4c8190b4aa4dee39efc0a6 |
completed | May 8, 2026, 10:24 p.m. |
Created at: April 28, 2026, 1:52 a.m.