Triple
T19782958
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | African Parks |
E475182
|
entity |
| Predicate | hasTrendInRecentYears |
P5318
|
FINISHED |
| Object | expansion to additional parks and countries |
—
|
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: expansion to additional parks and countries | Statement: [African Parks, hasTrendInRecentYears, expansion to additional parks and countries]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTrendInRecentYears Context triple: [African Parks, hasTrendInRecentYears, expansion to additional parks and countries]
-
A.
hasTrend
chosen
Indicates that something exhibits or is associated with a particular pattern of change or direction over time.
-
B.
hasTendency
Indicates that an entity is inclined or likely to exhibit a particular behavior, characteristic, or outcome under certain conditions.
-
C.
isFrequentlyRecordedSinceDecade
Indicates that an entity has been commonly or regularly recorded starting from a specified decade.
-
D.
hasOnlineTrendType
Indicates that an entity is associated with a specific category or type of online trend.
-
E.
sizeTrend
Indicates how the size of an entity changes over time or relative to another entity (e.g., increasing, decreasing, or remaining stable).
- 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_69d8e51b014081908b263e167370529a |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e653852e848190b8971981a164e8f9 |
completed | April 20, 2026, 4:25 p.m. |
| PD | Predicate disambiguation | batch_69e53053ed2881908400becdfada7fd3 |
completed | April 19, 2026, 7:43 p.m. |
Created at: April 10, 2026, 1:49 p.m.