Triple
T11816287
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lady Penrhyn |
E281007
|
entity |
| Predicate | numberOfConvictsCarried |
P101461
|
FINISHED |
| Object | 101 |
—
|
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: 101 | Statement: [Lady Penrhyn, numberOfConvictsCarried, 101]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfConvictsCarried Context triple: [Lady Penrhyn, numberOfConvictsCarried, 101]
-
A.
carriedPrisonersFrom
Indicates that an entity transported prisoners away from a specified origin location or source.
-
B.
numberOfPrisonersApproximate
Indicates an approximate count of prisoners associated with an entity or situation, rather than an exact number.
-
C.
numberOfConvictions
Indicates the count of times an entity has been formally found guilty of an offense.
-
D.
estimatedPrisonerCount
Indicates the estimated number of prisoners associated with a particular context, such as a location, time period, or event.
-
E.
convictedIndividual
Indicates that an individual has been found guilty of a crime or offense through a formal legal process and has received a conviction.
- F. None of above. chosen
Provenance (4 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_69d6ab26aae88190b2489efcb2a24234 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8a658f918819092c2db05fe2ab0ce |
completed | April 10, 2026, 7:27 a.m. |
| PD | Predicate disambiguation | batch_69d8a24e9a088190aff7932d1ff93dbf |
completed | April 10, 2026, 7:10 a.m. |
| PDg | Predicate description generation | batch_69d8a6574b7081908f7451d2bb233967 |
completed | April 10, 2026, 7:27 a.m. |
Created at: April 8, 2026, 9:42 p.m.