Triple
T3043522
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | E-Day |
E83186
|
entity |
| Predicate | marketingCharacterization |
P21662
|
FINISHED |
| Object | heavily promoted |
—
|
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: heavily promoted | Statement: [E-Day, marketingCharacterization, heavily promoted]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: marketingCharacterization Context triple: [E-Day, marketingCharacterization, heavily promoted]
-
A.
mediaCharacterization
Indicates how an entity is portrayed, described, or framed by media sources in terms of attributes, tone, or narrative.
-
B.
marketingAs
chosen
Indicates that one entity is being presented, promoted, or branded to others as if it were another specified entity, role, or category.
-
C.
characterizedBy
Indicates that one entity possesses a defining quality, feature, or attribute expressed by another entity.
-
D.
legalCharacterization
Indicates how an action, event, or situation is classified or characterized under a specific legal framework or set of laws.
-
E.
serviceCharacterization
Indicates how a service is defined, described, or classified in terms of its properties, behavior, or role.
- 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_69ad8b2298908190a7cb4e9bdbf064d0 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9b5ec5988190b8b6c95c743c6d1e |
completed | March 8, 2026, 3:53 p.m. |
| PD | Predicate disambiguation | batch_69ad961fc62c819087c4c3a44b00847d |
completed | March 8, 2026, 3:30 p.m. |
Created at: March 8, 2026, 3:01 p.m.