Triple
T6908478
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | S&P Latin America 40 |
E159870
|
entity |
| Predicate | targetConstituents |
P74055
|
FINISHED |
| Object | leading companies 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: leading companies in Latin America | Statement: [S&P Latin America 40, targetConstituents, leading companies in Latin America]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: targetConstituents Context triple: [S&P Latin America 40, targetConstituents, leading companies in Latin America]
-
A.
targetConstituency
Indicates that something is directed toward, intended for, or affects a particular constituency or group of stakeholders.
-
B.
targetsGroup
Indicates that an action, influence, or effect is directed toward a specific group as its intended recipient or focus.
-
C.
targetsUseCase
Indicates that one entity is aimed at or designed to address a particular use case associated with another entity.
-
D.
numberOfConstituents
Indicates the total count of individual components or members that make up a larger whole or group.
-
E.
targetConcern
Indicates that something is the specific issue, problem, or subject that is the focus of attention, action, or consideration.
- 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_69c68839ccb88190b4aa5cc1aca3448f |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6d9be98748190b5cb698e66e3aa42 |
completed | March 27, 2026, 7:25 p.m. |
| PD | Predicate disambiguation | batch_69c6d7b93d688190a297244ce81b67ac |
completed | March 27, 2026, 7:17 p.m. |
| PDg | Predicate description generation | batch_69c6d8c48ba48190b8d3aa7b8d22816b |
completed | March 27, 2026, 7:21 p.m. |
Created at: March 27, 2026, 2:25 p.m.