Triple
T16864103
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Operation Tuleta |
E409993
|
entity |
| Predicate | industryInvestigated |
P125273
|
FINISHED |
| Object | British press |
—
|
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: British press | Statement: [Operation Tuleta, industryInvestigated, British press]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: industryInvestigated Context triple: [Operation Tuleta, industryInvestigated, British press]
-
A.
industryContext
Indicates the industry or sector within which an entity, activity, or relationship is situated or most relevant.
-
B.
industryExposure
Indicates the extent to which an entity is involved in, affected by, or financially linked to a particular industry or set of industries.
-
C.
industryPerception
Indicates how an industry is viewed or regarded, typically in terms of reputation, trust, or overall public and stakeholder opinion.
-
D.
industryIntegrated
Indicates that something is closely connected or coordinated with industry practices, partners, or environments, often through collaboration, alignment, or direct involvement.
-
E.
investsIn
Indicates that one entity allocates resources, typically money or capital, into another entity with the expectation of future returns or benefits.
- 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_69d88395e6c88190b22730f335107c14 |
completed | April 10, 2026, 4:59 a.m. |
| NER | Named-entity recognition | batch_69e3b505a390819097ec31cd210eca60 |
completed | April 18, 2026, 4:44 p.m. |
| PD | Predicate disambiguation | batch_69e32b8cbb048190878a259cc5be960e |
completed | April 18, 2026, 6:58 a.m. |
| PDg | Predicate description generation | batch_69e355722040819098830dabf207ecd6 |
completed | April 18, 2026, 9:57 a.m. |
Created at: April 10, 2026, 5:24 a.m.