Triple
T33614242
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fairlawn Cemetery, Scio, New York |
E861071
|
entity |
| Predicate | hasNotableCategoryOfBurials |
P3803
|
FINISHED |
| Object | U.S. military veterans |
—
|
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: U.S. military veterans | Statement: [Fairlawn Cemetery, Scio, New York, hasNotableCategoryOfBurials, U.S. military veterans]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableCategoryOfBurials Context triple: [Fairlawn Cemetery, Scio, New York, hasNotableCategoryOfBurials, U.S. military veterans]
-
A.
hasNotableBurials
chosen
Indicates that a place, typically a cemetery or burial site, contains the graves or remains of individuals considered notable or significant.
-
B.
hasBurialsOf
Indicates that a location or site contains or includes the burial places of certain individuals or groups.
-
C.
hasBurialsFrom
Indicates that a location or site contains burials originating from a specified time period, culture, or source.
-
D.
hasNotablePeriodOfUseAsCemetery
Indicates that something was significantly used as a cemetery during a particular period of time.
-
E.
hasTypeOfBurial
Indicates the specific kind or method of burial associated with an 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_69f3498037c88190a4500f002b5540e0 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_6a01ccd995e48190b6675e463bd0a567 |
completed | May 11, 2026, 12:34 p.m. |
| PD | Predicate disambiguation | batch_6a01cc33177c8190bc0703276618b3ec |
completed | May 11, 2026, 12:31 p.m. |
Created at: May 1, 2026, 1:41 a.m.