Triple
T11595358
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | University System of Maryland |
E274985
|
entity |
| Predicate | numberOfRegionalCenters |
P63968
|
FINISHED |
| Object | 3 |
—
|
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: 3 | Statement: [University System of Maryland, numberOfRegionalCenters, 3]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfRegionalCenters Context triple: [University System of Maryland, numberOfRegionalCenters, 3]
-
A.
numberOfDistributionCenters
Indicates the quantity of distribution centers associated with a given entity.
-
B.
hasNumberOfCentres
chosen
Indicates the relationship specifying how many centers (or central units/locations) are associated with a given entity.
-
C.
numberOfRegions
Indicates the total count of distinct regions associated with or contained within a given entity.
-
D.
hasRegionalCentresIn
Indicates that an entity maintains one or more regional centers located in the specified place or area.
-
E.
hasNumberOfRegionalOffices
Indicates the quantity of regional offices that an entity possesses or operates.
- 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_69d6aae6b14c81908dc5a74bad7591f9 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8946790d08190924d60bb4b523250 |
completed | April 10, 2026, 6:10 a.m. |
| PD | Predicate disambiguation | batch_69d85dd20d188190863d1190d4c16048 |
completed | April 10, 2026, 2:17 a.m. |
Created at: April 8, 2026, 9:38 p.m.