Triple
T6281359
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Iberia’s Latin American network |
E140787
|
entity |
| Predicate | targetSegments |
P10541
|
FINISHED |
| Object | business travelers |
—
|
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: business travelers | Statement: [Iberia’s Latin American network, targetSegments, business travelers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: targetSegments Context triple: [Iberia’s Latin American network, targetSegments, business travelers]
-
A.
targetsGroup
chosen
Indicates that an action, influence, or effect is directed toward a specific group as its intended recipient or focus.
-
B.
targetsUseCase
Indicates that one entity is aimed at or designed to address a particular use case associated with another entity.
-
C.
intendedTargets
Indicates that an action, message, or object is specifically directed toward or meant to affect particular target entities.
-
D.
targetsSector
Indicates that an entity is directed toward, focused on, or intended to affect a particular economic or industry sector.
-
E.
targetMarket
Indicates the group of consumers or organizations that a product, service, or campaign is specifically intended and designed to reach.
- 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_69c008cd17c8819082b82d3fbeb68047 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c063dee62881908347283f16dcbe68 |
completed | March 22, 2026, 9:49 p.m. |
| PD | Predicate disambiguation | batch_69c05608a5608190b22a1fdc4060470d |
completed | March 22, 2026, 8:50 p.m. |
Created at: March 22, 2026, 4:26 p.m.