Triple
T28751223
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nagoya 5000 series |
E731532
|
entity |
| Predicate | laterUsedInCity |
P195387
|
FINISHED |
| Object | Buenos Aires |
—
|
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: Buenos Aires | Statement: [Nagoya 5000 series, laterUsedInCity, Buenos Aires]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: laterUsedInCity Context triple: [Nagoya 5000 series, laterUsedInCity, Buenos Aires]
-
A.
usedInCity
Indicates that something is utilized, applied, or operates within the context or boundaries of a particular city.
-
B.
usedCity
Indicates that an entity made use of or operated within a particular city as part of its activities or functions.
-
C.
usedInTown
Indicates that something is utilized, applied, or functions within the context or boundaries of a particular town.
-
D.
usedInCapitalCityOf
Indicates that something is utilized or applied within the capital city of a specified region or country.
-
E.
usedInProvincialCapital
Indicates that something is utilized or occurs within the administrative capital city of a province.
- 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_69f043ed68a881909e858a06bab7a247 |
completed | April 28, 2026, 5:21 a.m. |
| NER | Named-entity recognition | batch_69fdd07a34c08190982b8c61c2775cf6 |
completed | May 8, 2026, noon |
| PD | Predicate disambiguation | batch_69fdbd25c7908190b72fca8de7ce503f |
completed | May 8, 2026, 10:38 a.m. |
| PDg | Predicate description generation | batch_69fdd07724f88190a33ec602642d2ea3 |
completed | May 8, 2026, noon |
Created at: April 28, 2026, 6:07 a.m.