Triple
T973962
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anna M. Kross Center |
E21007
|
entity |
| Predicate | primaryInmateGender |
P72
|
FINISHED |
| Object | male |
—
|
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: male | Statement: [Anna M. Kross Center, primaryInmateGender, male]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryInmateGender Context triple: [Anna M. Kross Center, primaryInmateGender, male]
-
A.
prisonType
Indicates the specific category or classification of a prison associated with an entity.
-
B.
admissionGender
Indicates the gender-based criteria or classification applied in the context of admission or entry decisions.
-
C.
sexOrGender
chosen
Indicates that one entity has a specified biological sex or socially constructed gender identity.
-
D.
estimatedPrisonerCount
Indicates the estimated number of prisoners associated with a particular context, such as a location, time period, or event.
-
E.
numberOfPrisonersApproximate
Indicates an approximate count of prisoners associated with an entity or situation, rather than an exact number.
- 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_69a493c2b62c8190b616351789ec47f8 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b45f28f081908d41b2d7f353708d |
completed | March 1, 2026, 9:49 p.m. |
| PD | Predicate disambiguation | batch_69a4b2a6aa2c8190aebba71320ab678f |
completed | March 1, 2026, 9:41 p.m. |
Created at: March 1, 2026, 7:40 p.m.