Triple
T9550367
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MSCI USA Index |
E230404
|
entity |
| Predicate | coverageStyle |
P63754
|
FINISHED |
| Object | broad market large and mid cap |
—
|
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: broad market large and mid cap | Statement: [MSCI USA Index, coverageStyle, broad market large and mid cap]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: coverageStyle Context triple: [MSCI USA Index, coverageStyle, broad market large and mid cap]
-
A.
coverageModel
Indicates a relationship where a specific model or framework defines, structures, or governs the extent and manner in which something is covered or addressed.
-
B.
knownForCoverageStyle
chosen
Indicates that an entity is recognized or distinguished by a particular style or manner of coverage (e.g., how it reports on or presents information or events).
-
C.
coverageScope
Indicates the extent or range of entities, conditions, or situations that are included under a particular coverage or applicability.
-
D.
componentCoverage
Indicates that one entity provides coverage, protection, or support for another as one of its components or constituent parts.
-
E.
formatCoverage
Indicates how thoroughly or extensively a particular format or formatting scheme is applied or supported in a given 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_69ca847d3be8819099c9dad2a7e786f1 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9906bc90819086f105c453e63c83 |
completed | April 1, 2026, 10:15 p.m. |
| PD | Predicate disambiguation | batch_69ccd58bd21881908b860e3ee469af13 |
completed | April 1, 2026, 8:21 a.m. |
Created at: March 30, 2026, 8:02 p.m.