Triple
T5587950
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1954 Guatemalan coup d’état |
E146800
|
entity |
| Predicate | effectOnRegion |
P19397
|
FINISHED |
| Object | precedent for U.S.-backed coups in Latin America |
—
|
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: precedent for U.S.-backed coups in Latin America | Statement: [1954 Guatemalan coup d’état, effectOnRegion, precedent for U.S.-backed coups in Latin America]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: effectOnRegion Context triple: [1954 Guatemalan coup d’état, effectOnRegion, precedent for U.S.-backed coups in Latin America]
-
A.
impactRegion
Indicates the geographic or spatial area that is affected or influenced by a particular event, action, or phenomenon.
-
B.
influencesRegion
chosen
Indicates that one entity has an effect on, shapes, or alters the conditions, characteristics, or behavior of a specified region.
-
C.
affectedArea
Indicates the specific region or extent over which an event, condition, or influence has an impact.
-
D.
placeOfEffect
Indicates the location or setting where an action, event, or effect takes place or is realized.
-
E.
economicImpactRegion
Indicates the region or geographic area that experiences or is affected by a particular economic impact.
- 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.