Triple
T7586874
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Special Operations Division (Prince George’s County Police Department) |
E179634
|
entity |
| Predicate | mayIncludeUnitType |
P78005
|
FINISHED |
| Object | SWAT team |
—
|
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: SWAT team | Statement: [Special Operations Division (Prince George’s County Police Department), mayIncludeUnitType, SWAT team]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mayIncludeUnitType Context triple: [Special Operations Division (Prince George’s County Police Department), mayIncludeUnitType, SWAT team]
-
A.
mayHaveUnit
Indicates that an entity can optionally be associated with a specific unit of measurement.
-
B.
basedUnitsInclude
Indicates that a composite or derived unit is defined using, and therefore includes, the specified base unit(s) in its construction.
-
C.
mayIncludeFeature
Indicates that one entity is allowed or able to contain, incorporate, or be associated with a particular feature.
-
D.
multipleUnit
Indicates that an entity is composed of or associated with more than one unit of the same type.
-
E.
hasUnitOf
Indicates that a quantity, measurement, or value is expressed in terms of a specific unit.
- 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_69c69f335248819093c1006f30513708 |
completed | March 27, 2026, 3:16 p.m. |
| NER | Named-entity recognition | batch_69c6f9970efc8190b1b9286d86331359 |
completed | March 27, 2026, 9:41 p.m. |
| PD | Predicate disambiguation | batch_69c6f4e04c2c8190a889d928515d9b8e |
completed | March 27, 2026, 9:21 p.m. |
| PDg | Predicate description generation | batch_69c6f8184bb08190b2f70545a6aa277c |
completed | March 27, 2026, 9:35 p.m. |
Created at: March 27, 2026, 3:52 p.m.