Triple
T6518831
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jewish exodus from Arab and Muslim countries |
E148328
|
entity |
| Predicate | estimatedNumberOfPeople |
P2307
|
FINISHED |
| Object | around 850,000 Jews |
—
|
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 850,000 Jews | Statement: [Jewish exodus from Arab and Muslim countries, estimatedNumberOfPeople, around 850,000 Jews]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: estimatedNumberOfPeople Context triple: [Jewish exodus from Arab and Muslim countries, estimatedNumberOfPeople, around 850,000 Jews]
-
A.
guestCountApproximate
Indicates that the number of guests involved is represented as an estimated or approximate count rather than an exact figure.
-
B.
estimatedMemberCount
Indicates the approximate or predicted number of members associated with an entity.
-
C.
numberOfPersons
chosen
Indicates the total count of individual persons associated with or involved in a given entity, event, or context.
-
D.
peopleCountDescriptor
Indicates how the number of people involved in a situation, group, or context is characterized or described.
-
E.
passengersCountApproximate
Indicates that the number of passengers involved is given as an approximate or estimated count rather than an exact figure.
- 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_69c687e68e748190baceb9298f32d3ed |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6ac11d0e481908103c4b51de9521e |
completed | March 27, 2026, 4:10 p.m. |
| PD | Predicate disambiguation | batch_69c68abbc7148190a8270d47fe10cc31 |
completed | March 27, 2026, 1:48 p.m. |
Created at: March 27, 2026, 1:44 p.m.