Triple
T31674921
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MSCI ACWI Index |
E808373
|
entity |
| Predicate | numberOfCountriesCovered |
P3809
|
FINISHED |
| Object | over 45 |
—
|
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: over 45 | Statement: [MSCI ACWI Index, numberOfCountriesCovered, over 45]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfCountriesCovered Context triple: [MSCI ACWI Index, numberOfCountriesCovered, over 45]
-
A.
hasNumberOfCountries
chosen
Indicates the relationship that specifies how many countries are associated with or contained within a given entity.
-
B.
territoriesCovered
Indicates that one entity includes, spans, or encompasses the geographic or jurisdictional areas associated with another entity.
-
C.
dataCoverage
Indicates the extent or proportion of relevant data that is included, captured, or represented within a given dataset or system.
-
D.
widelyCoveredBy
Indicates that something (such as an event, topic, or issue) receives extensive attention or reporting from many media outlets or information sources.
-
E.
hasLandCoverage
Indicates that a specified area or region is covered or occupied by a particular type of land surface or land use.
- 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_69f348dcf5d48190ac25b1365ae717a8 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_6a01302a80ec8190a27692f09ac38a80 |
completed | May 11, 2026, 1:26 a.m. |
| PD | Predicate disambiguation | batch_6a012fad62308190a53f8b4a071eb245 |
completed | May 11, 2026, 1:23 a.m. |
Created at: April 30, 2026, 11:02 p.m.