Triple
T27753111
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Serengeti wildebeest migration |
E701262
|
entity |
| Predicate | approximateNumberOfGazelles |
P6210
|
FINISHED |
| Object | around 500,000 |
—
|
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: around 500,000 | Statement: [Serengeti wildebeest migration, approximateNumberOfGazelles, around 500,000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approximateNumberOfGazelles Context triple: [Serengeti wildebeest migration, approximateNumberOfGazelles, around 500,000]
-
A.
approximateNumberOfZebra
Indicates that one entity specifies an estimated or approximate count of zebras associated with another entity.
-
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.
hasApproximateNumberOfSwans
Indicates that an entity is associated with an estimated or non-exact quantity of swans.
-
D.
hasHerdSize
Indicates the number of individual animals that belong to a particular herd.
-
E.
numberOfAnimals
chosen
Indicates the quantity of animals associated with a given entity or 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_69ef6a5193808190816eb7d0020b2d87 |
completed | April 27, 2026, 1:53 p.m. |
| NER | Named-entity recognition | batch_69fcd867f36081908c88c55a6a1404c1 |
completed | May 7, 2026, 6:22 p.m. |
| PD | Predicate disambiguation | batch_69fcd1f47b188190b4cf4b4c748d9d03 |
completed | May 7, 2026, 5:55 p.m. |
Created at: April 27, 2026, 4:21 p.m.