Triple
T29009765
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Century Regional Detention Facility |
E736531
|
entity |
| Predicate | primaryGenderHoused |
P62762
|
FINISHED |
| Object | female |
—
|
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: female | Statement: [Century Regional Detention Facility, primaryGenderHoused, female]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryGenderHoused Context triple: [Century Regional Detention Facility, primaryGenderHoused, female]
-
A.
genderOfResidents
chosen
Indicates the gender identity or classification associated with the residents of a particular place or group.
-
B.
genderRatio
Indicates the proportional relationship between different genders within a given group or population.
-
C.
hasGenderRatioMale
Indicates the proportion or percentage of male individuals within a given population or group.
-
D.
hasGenderOfPerson
Indicates that a person is associated with a specific gender classification.
-
E.
hasGenderDesignation
Indicates that an entity is assigned or associated with a specific gender classification or label.
- 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_69f077eb81e88190ad9ff62cbb9f555e |
completed | April 28, 2026, 9:03 a.m. |
| NER | Named-entity recognition | batch_6a00a602b6b48190a9dea8ae22d2fa05 |
completed | May 10, 2026, 3:36 p.m. |
| PD | Predicate disambiguation | batch_6a00a559bf3881909a7b50776d8f47bb |
completed | May 10, 2026, 3:33 p.m. |
Created at: April 28, 2026, 9:41 a.m.