Triple
T2744817
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Luxembourg American Cemetery and Memorial |
E60840
|
entity |
| Predicate | burialCount |
P14555
|
FINISHED |
| Object | over 5000 |
—
|
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 5000 | Statement: [Luxembourg American Cemetery and Memorial, burialCount, over 5000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: burialCount Context triple: [Luxembourg American Cemetery and Memorial, burialCount, over 5000]
-
A.
numberOfBurials
chosen
Indicates the total count of burial events associated with a given entity.
-
B.
cemeteryBurialsSince
Indicates the number of burials that have occurred in a cemetery from a specified point in time onward.
-
C.
burials
Indicates that one entity is interred or laid to rest in a grave or burial site associated with another entity.
-
D.
burialBy
Indicates that one entity is responsible for burying or interring another entity.
-
E.
hasNotableBurials
Indicates that a place, typically a cemetery or burial site, contains the graves or remains of individuals considered notable or significant.
- 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_69ab4b79846081909096725374d65ce9 |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdb4d37a481908cc2ad4666f3ac94 |
completed | March 7, 2026, 8:01 a.m. |
| PD | Predicate disambiguation | batch_69abd829f1e88190aab1d54f87c69714 |
completed | March 7, 2026, 7:47 a.m. |
Created at: March 6, 2026, 9:56 p.m.