Triple
T6620933
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Evergreen Cemetery |
E149670
|
entity |
| Predicate | distanceToGettysburgNationalCemetery |
P71565
|
FINISHED |
| Object | adjacent, separated mainly by boundary lines and roads |
—
|
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: adjacent, separated mainly by boundary lines and roads | Statement: [Evergreen Cemetery, distanceToGettysburgNationalCemetery, adjacent, separated mainly by boundary lines and roads]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToGettysburgNationalCemetery Context triple: [Evergreen Cemetery, distanceToGettysburgNationalCemetery, adjacent, separated mainly by boundary lines and roads]
-
A.
distanceFromFord’sTheatre
Indicates the spatial distance between an entity and Ford’s Theatre.
-
B.
distanceToGreatSmokyMountainsNationalPark
Indicates the spatial distance between an entity and Great Smoky Mountains National Park.
-
C.
dayOfKeyGettysburgAction
Indicates the specific calendar day on which a key action or event related to the Battle of Gettysburg occurred.
-
D.
distanceToWashingtonMonument
Indicates the physical distance between a given entity’s location and the Washington Monument.
-
E.
distanceToLexington
Indicates the measured or calculated distance between a given entity and the location named Lexington.
- F. None of above. chosen
Provenance (4 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_69c687ed8a9c81908bb671717cb192ef |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6bdb88cc881908f35648c15a7dc85 |
completed | March 27, 2026, 5:26 p.m. |
| PD | Predicate disambiguation | batch_69c6ad007c1c8190af425f51011c7ad1 |
completed | March 27, 2026, 4:14 p.m. |
| PDg | Predicate description generation | batch_69c6bdb76ec48190b59d576170970cc9 |
completed | March 27, 2026, 5:26 p.m. |
Created at: March 27, 2026, 1:58 p.m.