Triple
T3065070
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Macworld 2007 |
E62083
|
entity |
| Predicate | impactOnIndustry |
P44194
|
FINISHED |
| Object | catalyzed modern smartphone era |
—
|
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: catalyzed modern smartphone era | Statement: [Macworld 2007, impactOnIndustry, catalyzed modern smartphone era]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: impactOnIndustry Context triple: [Macworld 2007, impactOnIndustry, catalyzed modern smartphone era]
-
A.
impactOnMarket
Indicates the effect or influence that one factor, event, or action has on market conditions, behavior, or outcomes.
-
B.
industryContext
Indicates the industry or sector within which an entity, activity, or relationship is situated or most relevant.
-
C.
sectorInfluence
Indicates the degree to which one sector affects, shapes, or exerts control over another sector or over outcomes within that sector.
-
D.
industryPerception
Indicates how an industry is viewed or regarded, typically in terms of reputation, trust, or overall public and stakeholder opinion.
-
E.
covid19Impact
Indicates the effect, consequences, or influence that COVID-19 has on a given entity, condition, or situation.
- 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_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. |
| PDg | Predicate description generation | batch_69ad97f7630c81908e1ca8a69611cff6 |
completed | March 8, 2026, 3:38 p.m. |
Created at: March 8, 2026, 3:02 p.m.