Triple
T594809
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bibliotheca Alexandrina |
E17356
|
entity |
| Predicate | readingRoomArea |
P175
|
FINISHED |
| Object | approximately 70000 square meters |
—
|
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 70000 square meters | Statement: [Bibliotheca Alexandrina, readingRoomArea, approximately 70000 square meters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: readingRoomArea Context triple: [Bibliotheca Alexandrina, readingRoomArea, approximately 70000 square meters]
-
A.
hasReadingRoom
Indicates that a place or facility includes a designated reading room area available for use.
-
B.
library
Indicates that an entity functions as or is associated with a library, typically as a place or system for storing, organizing, and providing access to collections of information resources.
-
C.
navigationArea
Indicates that a specified region or space is designated for movement, routing, or pathfinding within an environment.
-
D.
hasRoom
Indicates that an entity possesses, contains, or is associated with a specific room.
-
E.
area
chosen
Indicates that one entity has a measured two-dimensional extent or surface size quantified by another entity.
- 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_69a49379d09c8190ac7e00b24e2810b1 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a49bd280ac8190b6a530ce73da85c8 |
completed | March 1, 2026, 8:04 p.m. |
| PD | Predicate disambiguation | batch_69a494ceeb7881909a91ed1a35d5bf0a |
completed | March 1, 2026, 7:34 p.m. |
Created at: March 1, 2026, 7:33 p.m.