Triple
T1600173
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Granary Burying Ground |
E34372
|
entity |
| Predicate | numberOfGraves |
P14555
|
FINISHED |
| Object | over 2300 markers |
—
|
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 2300 markers | Statement: [Granary Burying Ground, numberOfGraves, over 2300 markers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfGraves Context triple: [Granary Burying Ground, numberOfGraves, over 2300 markers]
-
A.
numberOfBurials
chosen
Indicates the total count of burial events associated with a given entity.
-
B.
numberOfCemeteries
Indicates the count of cemeteries associated with a given entity or within a specified area.
-
C.
hasNotableBurials
Indicates that a place, typically a cemetery or burial site, contains the graves or remains of individuals considered notable or significant.
-
D.
cemeteryBurialsSince
Indicates the number of burials that have occurred in a cemetery from a specified point in time onward.
-
E.
hasCemetery
Indicates that one entity possesses, contains, or includes a cemetery associated with it.
- 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_69a885fdcb9c819081ce6f0b8cd477dd |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a95b02cd448190be8e3db9a5a7bac0 |
completed | March 5, 2026, 10:29 a.m. |
| PD | Predicate disambiguation | batch_69a907c1cad08190b9728dd557f39aa0 |
completed | March 5, 2026, 4:34 a.m. |
Created at: March 4, 2026, 7:28 p.m.