Triple
T7360066
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Royal Red Cross |
E169722
|
entity |
| Predicate | campaignSpecific |
P77058
|
FINISHED |
| Object | false |
—
|
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: false | Statement: [Royal Red Cross, campaignSpecific, false]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: campaignSpecific Context triple: [Royal Red Cross, campaignSpecific, false]
-
A.
campaignType
Indicates the specific category or kind of campaign an entity is associated with or participates in.
-
B.
campaignTheme
Indicates the central idea or message that characterizes and unifies a particular campaign.
-
C.
campaignUse
Indicates that one entity employs or leverages another as part of a campaign or organized promotional effort.
-
D.
promotionalCampaign
Indicates a relationship where one entity organizes or runs a coordinated set of marketing activities aimed at promoting another entity, product, or service.
-
E.
campaignDirected
Indicates that a campaign is intentionally targeted or directed toward a specific entity or audience.
- 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_69c68a59f2288190877ca15c19b1e822 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f26d6d6081909c7272a9ccae0d97 |
completed | March 27, 2026, 9:11 p.m. |
| PD | Predicate disambiguation | batch_69c6f02d36108190bcb34a95e6a30bd7 |
completed | March 27, 2026, 9:01 p.m. |
| PDg | Predicate description generation | batch_69c6f26c050c8190a2d009b45d920490 |
completed | March 27, 2026, 9:11 p.m. |
Created at: March 27, 2026, 3:06 p.m.