Triple
T30483496
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MacBook Air |
E775650
|
entity |
| Predicate | targetMarketRegion |
P12320
|
FINISHED |
| Object | global |
—
|
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: global | Statement: [MacBook Air, targetMarketRegion, global]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: targetMarketRegion Context triple: [MacBook Air, targetMarketRegion, global]
-
A.
marketRegion
chosen
Indicates the geographic or demographic area in which a product, service, or entity is actively marketed or targeted.
-
B.
targetInvestorRegion
Indicates the geographic region or market area that an investor is focused on or intended to invest in.
-
C.
hasTargetAudienceRegion
Indicates that something is intended for or directed toward an audience located in a specific geographic region.
-
D.
typicalTargetRegion
Indicates the region or area that an action, process, or effect is most commonly directed toward or occurs in.
-
E.
campaignRegion
Indicates the geographic area or territory in which a campaign is conducted or targeted.
- 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_69f22497f91c8190afa7165bc900accd |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69fd49f6dbac81909744373a357b7982 |
completed | May 8, 2026, 2:27 a.m. |
| PD | Predicate disambiguation | batch_69fd48ed68f481908374183c66a6b055 |
completed | May 8, 2026, 2:22 a.m. |
Created at: April 29, 2026, 8:13 p.m.