Triple
T37840844
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Norman Hartnell (vintage gown loaned by Queen Elizabeth II) |
E943469
|
entity |
| Predicate | loanContext |
P189263
|
FINISHED |
| Object | loan from the wardrobe of Queen Elizabeth II |
—
|
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: loan from the wardrobe of Queen Elizabeth II | Statement: [Norman Hartnell (vintage gown loaned by Queen Elizabeth II), loanContext, loan from the wardrobe of Queen Elizabeth II]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: loanContext Context triple: [Norman Hartnell (vintage gown loaned by Queen Elizabeth II), loanContext, loan from the wardrobe of Queen Elizabeth II]
-
A.
loanSource
Indicates that one entity is the origin or provider of a loan extended to another entity.
-
B.
loanCharacteristic
Indicates that a loan possesses a specific attribute, feature, or quality that characterizes its terms or structure.
-
C.
loanStructure
Indicates the specific terms, components, and repayment arrangement that define how a loan is organized between parties.
-
D.
loanType
Indicates the specific category or kind of loan associated with an entity or transaction.
-
E.
lenderType
Indicates the classification or category of the lender involved in a lending relationship (e.g., bank, individual, institution).
- 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_69f76eeb0f7081908d6d3adbc469889c |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fbbae559a8819086ef839973f8d9b2 |
completed | May 6, 2026, 10:04 p.m. |
| PD | Predicate disambiguation | batch_69fbb1440fa08190abf25ba684f75b6e |
completed | May 6, 2026, 9:23 p.m. |
| PDg | Predicate description generation | batch_69fbbae3fc508190adff3d7abbf107a4 |
completed | May 6, 2026, 10:04 p.m. |
Created at: May 3, 2026, 4:19 p.m.