Triple
T12871671
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tassili Airlines |
E307863
|
entity |
| Predicate | focusCustomerSegment |
P481
|
FINISHED |
| Object | oil company employees |
—
|
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: oil company employees | Statement: [Tassili Airlines, focusCustomerSegment, oil company employees]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: focusCustomerSegment Context triple: [Tassili Airlines, focusCustomerSegment, oil company employees]
-
A.
customerFocus
Indicates that one entity prioritizes understanding and meeting the needs, preferences, or satisfaction of another entity (typically a customer or client).
-
B.
targetMarket
chosen
Indicates the group of consumers or organizations that a product, service, or campaign is specifically intended and designed to reach.
-
C.
brandSegment
Indicates the specific market segment or customer group that a brand is targeted toward or associated with.
-
D.
passengerSegments
Indicates a relationship where a journey or trip is divided into distinct legs or segments that a passenger travels through.
-
E.
marketSegmentCoverage
Indicates the extent to which a product, service, or campaign reaches or serves the intended market segment(s).
- 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_69d7bdf69bc48190af6c2621f28ca351 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d97c7f91d08190aac2f6419d3ba992 |
completed | April 10, 2026, 10:41 p.m. |
| PD | Predicate disambiguation | batch_69d96fa55b888190ab1612e93c41aec4 |
completed | April 10, 2026, 9:46 p.m. |
Created at: April 9, 2026, 5:38 p.m.