Triple
T420272
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carnegie libraries |
E8084
|
entity |
| Predicate | hasNumberOfBuildings |
P11484
|
FINISHED |
| Object | over 2500 worldwide |
—
|
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: over 2500 worldwide | Statement: [Carnegie libraries, hasNumberOfBuildings, over 2500 worldwide]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfBuildings Context triple: [Carnegie libraries, hasNumberOfBuildings, over 2500 worldwide]
-
A.
numberOfBuildings
chosen
Indicates the total count of buildings associated with a given entity or within a specified context.
-
B.
hasNumberOfHouses
Indicates the quantity of houses associated with a given entity.
-
C.
numberOfHouses
Indicates the quantity of houses associated with a given entity or context.
-
D.
numberOfFloors
Indicates the total count of distinct floor levels that a building or structure has.
-
E.
isPublicBuilding
Indicates that a building is designated for public use, access, or service rather than being privately owned or restricted.
- 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_69a2e7f1d1bc81909cf2dc9754a3c334 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2eebde1d881908fb212bfba9d7c67 |
completed | Feb. 28, 2026, 1:33 p.m. |
| PD | Predicate disambiguation | batch_69a2edd3b948819097d96c73d0a0f699 |
completed | Feb. 28, 2026, 1:29 p.m. |
Created at: Feb. 28, 2026, 1:11 p.m.