Triple
T1676270
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leave.EU |
E36237
|
entity |
| Predicate | usedCampaignTechnique |
P3047
|
FINISHED |
| Object | online targeted advertising |
—
|
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: online targeted advertising | Statement: [Leave.EU, usedCampaignTechnique, online targeted advertising]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedCampaignTechnique Context triple: [Leave.EU, usedCampaignTechnique, online targeted advertising]
-
A.
hasTechnique
chosen
Indicates that an entity employs, utilizes, or is associated with a particular method, procedure, or technique.
-
B.
marketingUse
Indicates that something is used for marketing purposes, such as promotion, advertising, or brand communication.
-
C.
usedAgainst
Indicates that one entity is employed, applied, or deployed in opposition to, or for the purpose of affecting, another entity.
-
D.
effectOnCampaign
Indicates the influence or impact that one factor has on the outcome or performance of a campaign.
-
E.
usesIntervention
Indicates that one entity applies, employs, or relies on a specific intervention (such as a treatment, method, or strategy) in relation to another entity or context.
- 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_69a886139ed081909af0940aa9313512 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69ab272a653481908f48aa1eed5de8a4 |
completed | March 6, 2026, 7:12 p.m. |
| PD | Predicate disambiguation | batch_69aa61b2f6288190b2348ef7d7e4672d |
completed | March 6, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:29 p.m.