Triple
T8513427
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shawshank State Penitentiary |
E201511
|
entity |
| Predicate | hasFictionalFacilityType |
P2836
|
FINISHED |
| Object | cell blocks |
—
|
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: cell blocks | Statement: [Shawshank State Penitentiary, hasFictionalFacilityType, cell blocks]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalFacilityType Context triple: [Shawshank State Penitentiary, hasFictionalFacilityType, cell blocks]
-
A.
hasFictionalEstablishmentType
Indicates that an establishment is associated with a particular type or category of fictional setting or institution.
-
B.
hasFacilityType
chosen
Indicates that an entity possesses or is associated with a specific type or category of facility.
-
C.
designedFacilityType
Indicates the type or category of facility that something (such as a plan, system, or component) is specifically designed for.
-
D.
fictionalHospital
Indicates that a hospital is imaginary or exists only within a fictional or narrative context, rather than in reality.
-
E.
isNamedFacilityOf
Indicates that a facility bears the official name associated with a particular entity (such as an organization, person, or place).
- 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_69ca8320e5748190ac2c585a0bba8193 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe60cdfcc819081a9be1229378ba0 |
completed | March 31, 2026, 3:19 p.m. |
| PD | Predicate disambiguation | batch_69cbd10cfd208190a519049fad32c508 |
completed | March 31, 2026, 1:50 p.m. |
Created at: March 30, 2026, 6:15 p.m.