Triple
T14707451
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fox River State Penitentiary |
E345461
|
entity |
| Predicate | hasInmateNumberForCharacter |
P70633
|
FINISHED |
| Object | Lincoln Burrows – inmate number 94941 |
—
|
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: Lincoln Burrows – inmate number 94941 | Statement: [Fox River State Penitentiary, hasInmateNumberForCharacter, Lincoln Burrows – inmate number 94941]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasInmateNumberForCharacter Context triple: [Fox River State Penitentiary, hasInmateNumberForCharacter, Lincoln Burrows – inmate number 94941]
-
A.
prisonerNumber
chosen
Indicates that an entity is assigned a specific identification number used to uniquely identify them as a prisoner.
-
B.
laterPrisonerNumber
Indicates that one prisoner has a higher (and thus later-assigned) prisoner identification number than another prisoner.
-
C.
hasBailiffCharacter
Indicates that one entity is characterized as, or fulfills the role of, a bailiff in relation to another entity.
-
D.
hasInmateGender
Indicates that an inmate possesses a specified gender.
-
E.
hasChamberNumber
Indicates that an entity is associated with a specific chamber identified by a particular 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_69d822e4a8c08190a155df736bb7bc13 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb609965081908f654bcb9eaaa145 |
completed | April 14, 2026, 9:47 p.m. |
| PD | Predicate disambiguation | batch_69de657c57ec8190ae0b9bb79a514566 |
completed | April 14, 2026, 4:04 p.m. |
Created at: April 10, 2026, 1:28 a.m.