Triple
T3065071
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Macworld 2007 |
E62083
|
entity |
| Predicate | impactOnCompany |
P17342
|
FINISHED |
| Object | marked major strategic shift toward mobile devices for Apple |
—
|
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: marked major strategic shift toward mobile devices for Apple | Statement: [Macworld 2007, impactOnCompany, marked major strategic shift toward mobile devices for Apple]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: impactOnCompany Context triple: [Macworld 2007, impactOnCompany, marked major strategic shift toward mobile devices for Apple]
-
A.
impactOnMarket
Indicates the effect or influence that one factor, event, or action has on market conditions, behavior, or outcomes.
-
B.
affectedCompany
chosen
Indicates that a company is impacted or influenced by a particular event, action, or entity.
-
C.
impactBuilding
Indicates that one entity physically collides with or strikes a building, causing an impact event.
-
D.
company
Indicates that an entity is a business organization engaged in commercial, industrial, or professional activities.
-
E.
impactOnLaw
Indicates the effect or influence that one entity, event, or action has on laws, legal rules, or the legal system.
- 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_69ad85793e5c8190a358049bc4a98d8c |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada0fc01dc81908fbdf7c1ef73afe4 |
completed | March 8, 2026, 4:17 p.m. |
| PD | Predicate disambiguation | batch_69ad9624b7a0819091d255614f5819ea |
completed | March 8, 2026, 3:30 p.m. |
Created at: March 8, 2026, 3:02 p.m.