Triple
T27189387
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maryland Route 17 |
E683427
|
entity |
| Predicate | passesNearMunicipality |
P3945
|
FINISHED |
| Object | Burkittsville, Maryland |
—
|
NE NERFINISHED |
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: Burkittsville, Maryland | Statement: [Maryland Route 17, passesNearMunicipality, Burkittsville, Maryland]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: passesNearMunicipality Context triple: [Maryland Route 17, passesNearMunicipality, Burkittsville, Maryland]
-
A.
passesNearCity
chosen
Indicates that the path, route, or trajectory of one entity goes close to, but not necessarily through, a specified city.
-
B.
isMunicipalityNear
Indicates that one municipality is located close to another municipality or geographic reference point.
-
C.
hasMunicipalitySeatNearby
Indicates that the municipality’s administrative seat is located in close proximity to the referenced place or entity.
-
D.
isInMunicipality
Indicates that one entity (typically a place or address) is located within the administrative boundaries of a specific municipality.
-
E.
passesCity
Indicates that a route, path, or journey goes through or traverses a particular city as part of its course.
- 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_69eefad140408190b8586fdebcf9af46 |
completed | April 27, 2026, 5:57 a.m. |
| NER | Named-entity recognition | batch_69f66c5c13808190887180099745673b |
completed | May 2, 2026, 9:27 p.m. |
| PD | Predicate disambiguation | batch_69f66abddc448190a488852f8abdeb2c |
completed | May 2, 2026, 9:21 p.m. |
Created at: April 27, 2026, 9:31 a.m.