Triple
T80582
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Martin Luther King Jr. Memorial, Washington, D.C. |
E1617
|
entity |
| Predicate | open |
P990
|
FINISHED |
| Object | year-round |
—
|
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: year-round | Statement: [Martin Luther King Jr. Memorial, Washington, D.C., open, year-round]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: open Context triple: [Martin Luther King Jr. Memorial, Washington, D.C., open, year-round]
-
A.
opened
Indicates that one entity caused another entity (such as an object, container, or passage) to change from a closed or inaccessible state to an open or accessible state.
-
B.
openedIn
Indicates that an entity (such as a business, event, or institution) began operating or was inaugurated in a specific time period or location.
-
C.
openedForUse
chosen
Indicates that something has been made accessible and available for its intended operation or use.
-
D.
export
Indicates that one entity sends goods, services, or data out from its own domain or territory to another entity or external destination.
-
E.
openingTheme
Indicates that one entity serves as the opening theme (such as a song or musical piece) for another entity, typically a show, series, or similar work.
- 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_69a24c60d19c8190a1b6c105ca59ef5b |
completed | Feb. 28, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69a25053ca208190a371b0d38000c2b9 |
completed | Feb. 28, 2026, 2:17 a.m. |
| PD | Predicate disambiguation | batch_69a24eb2998c819082681da74601d446 |
completed | Feb. 28, 2026, 2:10 a.m. |
Created at: Feb. 28, 2026, 2:06 a.m.