Triple
T37170680
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lady Trentham |
E920899
|
entity |
| Predicate | receivesIncomeFrom |
P9120
|
FINISHED |
| Object | allowance from Sylvia McCordle |
—
|
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: allowance from Sylvia McCordle | Statement: [Lady Trentham, receivesIncomeFrom, allowance from Sylvia McCordle]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: receivesIncomeFrom Context triple: [Lady Trentham, receivesIncomeFrom, allowance from Sylvia McCordle]
-
A.
usesForIncome
Indicates that one entity derives income or financial gain from using another entity.
-
B.
receivesPensionFrom
chosen
Indicates that one entity is the source or provider of a pension that another entity receives.
-
C.
incomeType
Indicates the category or source classification of an entity’s income within a given context.
-
D.
recipientOf
Indicates that one entity is the receiver or beneficiary of something (such as an item, message, or action) from another entity.
-
E.
income
Indicates the amount of money an entity receives, typically over a specified period, from work, investments, or other sources.
- 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_69f76ea16f288190b445aa1604d996f4 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fcf825ca7081909d06b0df33eb33f9 |
completed | May 7, 2026, 8:37 p.m. |
| PD | Predicate disambiguation | batch_69fcf42160f0819096812a8bf590875e |
completed | May 7, 2026, 8:20 p.m. |
Created at: May 3, 2026, 4:15 p.m.