Triple
T8427827
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | One Love |
E199044
|
entity |
| Predicate | chartImpact |
P83289
|
FINISHED |
| Object | produced multiple international top 10 singles |
—
|
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: produced multiple international top 10 singles | Statement: [One Love, chartImpact, produced multiple international top 10 singles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: chartImpact Context triple: [One Love, chartImpact, produced multiple international top 10 singles]
-
A.
encodingImpact
Indicates how one encoding or encoding choice affects, modifies, or constrains another process, representation, or outcome.
-
B.
impactCategory
Indicates the type or domain of effect that one entity or action has on another, classifying the nature of its impact.
-
C.
recognizesImpactOn
Indicates that one entity acknowledges or understands the effect or consequences it has on another entity or situation.
-
D.
impactOnMarket
Indicates the effect or influence that one factor, event, or action has on market conditions, behavior, or outcomes.
-
E.
impactBuilding
Indicates that one entity physically collides with or strikes a building, causing an impact event.
- 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_69ca8313c99081909a5c6d83b91de5b3 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe30fba4081908bfdef3faf5baceb |
completed | March 31, 2026, 3:06 p.m. |
| PD | Predicate disambiguation | batch_69cbd0ec200c8190b0299e2b0b4bdcc2 |
completed | March 31, 2026, 1:49 p.m. |
| PDg | Predicate description generation | batch_69cbe30c2d088190b4cb89adb4e88273 |
completed | March 31, 2026, 3:06 p.m. |
Created at: March 30, 2026, 6:07 p.m.