Triple
T27753109
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Serengeti wildebeest migration |
E701262
|
entity |
| Predicate | approximateNumberOfWildebeest |
P6210
|
FINISHED |
| Object | over 1,000,000 |
—
|
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 1,000,000 | Statement: [Serengeti wildebeest migration, approximateNumberOfWildebeest, over 1,000,000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approximateNumberOfWildebeest Context triple: [Serengeti wildebeest migration, approximateNumberOfWildebeest, over 1,000,000]
-
A.
hasWildPopulationOf
Indicates that a location or area contains a naturally occurring, non-captive population of the specified species.
-
B.
lionNumber
Indicates a relationship where a specific number is associated with or assigned to a lion (or lions), such as a count, identifier, or quantity.
-
C.
numberOfAnimals
chosen
Indicates the quantity of animals associated with a given entity or context.
-
D.
generaCountApproximate
Indicates an approximate or estimated number of genera associated with an entity.
-
E.
hasApproximateNumberOfSwans
Indicates that an entity is associated with an estimated or non-exact quantity of swans.
- 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_69ef6a5193808190816eb7d0020b2d87 |
completed | April 27, 2026, 1:53 p.m. |
| NER | Named-entity recognition | batch_69fcc7338120819081cb46547d60f2cb |
completed | May 7, 2026, 5:09 p.m. |
| PD | Predicate disambiguation | batch_69fcc58566a0819082d5ea36e03bf0c6 |
completed | May 7, 2026, 5:01 p.m. |
Created at: April 27, 2026, 4:21 p.m.