Triple
T1303562
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | District Court of Maryland |
E27821
|
entity |
| Predicate | hasNumberOfLocations |
P14032
|
FINISHED |
| Object | multiple locations statewide |
—
|
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: multiple locations statewide | Statement: [District Court of Maryland, hasNumberOfLocations, multiple locations statewide]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfLocations Context triple: [District Court of Maryland, hasNumberOfLocations, multiple locations statewide]
-
A.
numberOfSites
chosen
Indicates the total count of distinct sites associated with or involved in the given entity or context.
-
B.
numberOfVenues
Indicates the total count of venues associated with a given entity or context.
-
C.
hasDriveThroughLocations
Indicates that an entity operates or includes locations where customers can receive services or make purchases without leaving their vehicles.
-
D.
hasNumberOfCountries
Indicates the relationship that specifies how many countries are associated with or contained within a given entity.
-
E.
hasTenants
Indicates that an entity occupies or rents space from another entity as its tenant.
- 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_69a496d7d83481908f83085854e51328 |
completed | March 1, 2026, 7:43 p.m. |
| NER | Named-entity recognition | batch_69a4c116d7d881908ee631258f80980e |
completed | March 1, 2026, 10:43 p.m. |
| PD | Predicate disambiguation | batch_69a4bee8544c8190874efd9bae9bccf9 |
completed | March 1, 2026, 10:34 p.m. |
Created at: March 1, 2026, 7:51 p.m.