Triple
T13674888
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | National Historic Landmarks in Maryland |
E327847
|
entity |
| Predicate | hasAssociatedBenefits |
P75212
|
FINISHED |
| Object | technical preservation assistance |
—
|
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: technical preservation assistance | Statement: [National Historic Landmarks in Maryland, hasAssociatedBenefits, technical preservation assistance]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAssociatedBenefits Context triple: [National Historic Landmarks in Maryland, hasAssociatedBenefits, technical preservation assistance]
-
A.
hasBenefit
Indicates that one entity provides an advantage, improvement, or positive outcome to another entity.
-
B.
hasBenefitType
chosen
Indicates that an entity is associated with a specific category or type of benefit it provides or receives.
-
C.
benefitAppliesTo
Indicates that a particular benefit is applicable to, or valid for, a specified entity or context.
-
D.
benefitsAre
Indicates that certain advantages, gains, or positive outcomes are possessed by or accrue to a particular entity or group.
-
E.
interactionWithOtherBenefits
Indicates how this benefit relates to, depends on, or affects other benefits within the same system or context.
- 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_69d8076f1fa8819094664a59b55010df |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbc65c04988190b675e6fb7241e53c |
completed | April 12, 2026, 4:20 p.m. |
| PD | Predicate disambiguation | batch_69dbbe8d8d0881908d6e89954f44eed4 |
completed | April 12, 2026, 3:47 p.m. |
Created at: April 9, 2026, 9:53 p.m.