Triple
T5587948
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1954 Guatemalan coup d’état |
E146800
|
entity |
| Predicate | effectOnCountry |
P8692
|
FINISHED |
| Object | long-term human rights abuses in Guatemala |
—
|
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: long-term human rights abuses in Guatemala | Statement: [1954 Guatemalan coup d’état, effectOnCountry, long-term human rights abuses in Guatemala]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: effectOnCountry Context triple: [1954 Guatemalan coup d’état, effectOnCountry, long-term human rights abuses in Guatemala]
-
A.
effectOnUnitedStates
Indicates the impact, influence, or consequences that something has on the United States.
-
B.
affectedCountry
chosen
Indicates that a particular country is impacted or influenced by an event, action, or condition.
-
C.
economicImpactRegion
Indicates the region or geographic area that experiences or is affected by a particular economic impact.
-
D.
effectOnSpain
Indicates a relationship where one entity produces an influence, change, or consequence specifically affecting Spain.
-
E.
effectOnInstitution
Indicates the impact or influence that one entity, event, or action has on an institution’s state, functioning, or outcomes.
- 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_69c009036c408190981a8d690b679b67 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c0209e892c8190b936a05ef2a14d36 |
completed | March 22, 2026, 5:02 p.m. |
| PD | Predicate disambiguation | batch_69c01b16b9bc8190ab0b945507d90e05 |
completed | March 22, 2026, 4:38 p.m. |
Created at: March 22, 2026, 3:38 p.m.