Triple
T7595487
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United States Penitentiary, Terre Haute |
E179846
|
entity |
| Predicate | numberOfInmatesClass |
P39825
|
FINISHED |
| Object | high-security male inmates |
—
|
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: high-security male inmates | Statement: [United States Penitentiary, Terre Haute, numberOfInmatesClass, high-security male inmates]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfInmatesClass Context triple: [United States Penitentiary, Terre Haute, numberOfInmatesClass, high-security male inmates]
-
A.
numberOfPrisonersApproximate
Indicates an approximate count of prisoners associated with an entity or situation, rather than an exact number.
-
B.
estimatedPrisonerCount
Indicates the estimated number of prisoners associated with a particular context, such as a location, time period, or event.
-
C.
numberInClass
chosen
Indicates that a specified entity is a member of, or belongs to, a particular class or category.
-
D.
inmates
Indicates that one entity is confined or held as a prisoner within an institution or facility associated with another entity.
-
E.
numberOfPrisonSentences
Indicates the count of distinct prison sentences that have been imposed on a given individual or entity.
- 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_69c69f3487ec8190bf7acdf2dd91e6d6 |
completed | March 27, 2026, 3:16 p.m. |
| NER | Named-entity recognition | batch_69c6f9bbcd8081909a229d7faa2ffdc8 |
completed | March 27, 2026, 9:42 p.m. |
| PD | Predicate disambiguation | batch_69c6f4e2e42c8190afc802c4796c9cc2 |
completed | March 27, 2026, 9:21 p.m. |
Created at: March 27, 2026, 3:53 p.m.