Triple
T235977
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Istanbul |
E4825
|
entity |
| Predicate | areaTotalKm2 |
P157
|
FINISHED |
| Object | approximately 5340 |
—
|
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 5340 | Statement: [Istanbul, areaTotalKm2, approximately 5340]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: areaTotalKm2 Context triple: [Istanbul, areaTotalKm2, approximately 5340]
-
A.
landArea
chosen
Indicates the total surface area of a piece of land associated with an entity, typically measured in standardized units (e.g., square meters, hectares).
-
B.
metroArea
Indicates that one location is part of, or belongs to, a specified metropolitan area.
-
C.
areaWater
Indicates the relationship between a geographic entity and the total area of its surface that is covered by water.
-
D.
ianaArea
Indicates that one entity is associated with, or falls within, a specific IANA-defined geographic or administrative area represented by the other entity.
-
E.
hasLargestCountryByArea
Indicates that, among a set of compared entities, the subject is associated with the country that has the greatest land area.
- 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_69a257c3d0708190b0871c4269d273e6 |
completed | Feb. 28, 2026, 2:49 a.m. |
| NER | Named-entity recognition | batch_69a25ccab7648190be6e4f5febc1e313 |
completed | Feb. 28, 2026, 3:11 a.m. |
| PD | Predicate disambiguation | batch_69a25b5dc640819092669575731c393f |
completed | Feb. 28, 2026, 3:05 a.m. |
Created at: Feb. 28, 2026, 2:53 a.m.