Triple
T1159048
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Phanar district of Istanbul |
E24452
|
entity |
| Predicate | demographicHistory |
P25591
|
FINISHED |
| Object | historically Greek-speaking population |
—
|
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: historically Greek-speaking population | Statement: [Phanar district of Istanbul, demographicHistory, historically Greek-speaking population]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: demographicHistory Context triple: [Phanar district of Istanbul, demographicHistory, historically Greek-speaking population]
-
A.
historicalPopulationMovement
Indicates the movement or migration of a population from one place to another during a specific historical period or event.
-
B.
historicalMigrationType
Indicates the type or category of migration that occurred in a historical context between entities.
-
C.
demographicImpact
Indicates how an action, event, or condition affects the size, structure, or composition of a population.
-
D.
populationBottleneckPeriod
Indicates a time interval during which a population underwent a significant reduction in size, creating a demographic bottleneck.
-
E.
demographicScope
Indicates the specific population group or demographic segment to which something (e.g., a policy, study, product, or service) is targeted or applicable.
- F. None of above. chosen
Provenance (4 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_69a494060e148190abb42f971242c197 |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bcad47a08190895769611798f67f |
completed | March 1, 2026, 10:24 p.m. |
| PD | Predicate disambiguation | batch_69a4bb525b648190adcb7a29256d3c41 |
completed | March 1, 2026, 10:18 p.m. |
| PDg | Predicate description generation | batch_69a4bc49693c8190978ec63a5171d342 |
completed | March 1, 2026, 10:23 p.m. |
Created at: March 1, 2026, 7:45 p.m.