Triple
T6518708
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lydda and Ramle exodus |
E148326
|
entity |
| Predicate | estimatedNumberOfRefugees |
P1943
|
FINISHED |
| Object | approximately 50,000 to 70,000 |
—
|
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: approximately 50,000 to 70,000 | Statement: [Lydda and Ramle exodus, estimatedNumberOfRefugees, approximately 50,000 to 70,000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: estimatedNumberOfRefugees Context triple: [Lydda and Ramle exodus, estimatedNumberOfRefugees, approximately 50,000 to 70,000]
-
A.
approximateNumberOfRefugeesTransported
Indicates an estimated count of refugees who were transported in the described event or context.
-
B.
hasRefugeePopulation
Indicates that an entity hosts, contains, or is associated with a population of refugees.
-
C.
displacedPeopleEstimate
chosen
Indicates an estimated number of people who have been forced to leave their homes or usual places of residence due to a particular event or situation.
-
D.
estimatedEmigrants
Indicates the estimated number of people who have left a place or country to live elsewhere.
-
E.
hasRefugeeCamp
Indicates that a location or entity hosts, contains, or is the site of a refugee camp.
- 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_69c6ac108abc819082d1368af6611a92 |
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.