Triple
T38208706
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SFR Group |
E1009276
|
entity |
| Predicate | hasCallingCodeCoverage |
P202143
|
FINISHED |
| Object | +33 |
—
|
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: +33 | Statement: [SFR Group, hasCallingCodeCoverage, +33]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCallingCodeCoverage Context triple: [SFR Group, hasCallingCodeCoverage, +33]
-
A.
hasBranchCoverage
Indicates that a subject (such as a test suite or analysis) covers the decision branches of a target entity’s control flow (such as code or a model), meaning its conditional paths have been exercised.
-
B.
hasCall
Indicates that one entity initiates or participates in a telephone or voice communication with another entity.
-
C.
branchCoverage
Indicates that one entity measures or represents how many decision branches in another entity’s control flow have been executed during testing.
-
D.
hasCoverage
Indicates that one entity provides insurance or protection coverage for another entity or subject.
-
E.
componentCoverage
Indicates that one entity provides coverage, protection, or support for another as one of its components or constituent parts.
- 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_69f76dc94fcc8190bd2f55e81f9d6527 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_6a0058c341ac8190825067dc25158839 |
completed | May 10, 2026, 10:06 a.m. |
| PD | Predicate disambiguation | batch_6a005857249c81908b27587b84d84dbb |
completed | May 10, 2026, 10:05 a.m. |
| PDg | Predicate description generation | batch_6a0058c26638819085d8cbaa0bd4ccf0 |
completed | May 10, 2026, 10:06 a.m. |
Created at: May 3, 2026, 4:30 p.m.