Triple
T28453478
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Buenos Aires and Pacific Railway |
E716641
|
entity |
| Predicate | nationalizationBy |
P179921
|
FINISHED |
| Object | Government of Argentina |
—
|
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: Government of Argentina | Statement: [Buenos Aires and Pacific Railway, nationalizationBy, Government of Argentina]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nationalizationBy Context triple: [Buenos Aires and Pacific Railway, nationalizationBy, Government of Argentina]
-
A.
nationalizationReason
Indicates the reason or justification for which an entity is nationalized by a state or government.
-
B.
reasonForNationalization
Indicates the underlying cause or justification for which an entity was nationalized by a state.
-
C.
nationalised
Indicates that control or ownership of an entity has been transferred from private or non-state hands to the government or state.
-
D.
typeOfNationalisation
Indicates the specific form or method by which an asset, industry, or entity is brought under national or state ownership or control.
-
E.
nationalisationYear
Indicates the year in which an entity was taken into state ownership or control through nationalisation.
- 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_69f72921cf2c8190909bb53f78bcc890 |
completed | May 3, 2026, 10:53 a.m. |
| PD | Predicate disambiguation | batch_69f7283d8cec8190b524c144948bc4ec |
completed | May 3, 2026, 10:49 a.m. |
| PDg | Predicate description generation | batch_69f72920c6208190aa4aba6cb6193109 |
completed | May 3, 2026, 10:53 a.m. |
Created at: April 28, 2026, 1:53 a.m.