Triple
T7869690
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Federal Home Loan Bank Act |
E182706
|
entity |
| Predicate | numberOfOriginalBanks |
P63611
|
FINISHED |
| Object | 12 |
—
|
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: 12 | Statement: [Federal Home Loan Bank Act, numberOfOriginalBanks, 12]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfOriginalBanks Context triple: [Federal Home Loan Bank Act, numberOfOriginalBanks, 12]
-
A.
numberOfBanks
chosen
Indicates the quantity or count of banks associated with a given entity or context.
-
B.
numberOfATMs
Indicates the quantitative relationship specifying how many ATMs are associated with a given entity or location.
-
C.
numberOfTargetInstitutions
Indicates the count of institutions that are designated or identified as targets in a given context or dataset.
-
D.
numberOfFederalReserveBanks
Indicates the quantity of Federal Reserve Banks associated with or relevant to a given entity.
-
E.
numberOfFederalLandBanksCreated
Indicates the total count of federal land banks that were established or created in a given context.
- 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_69ca82894d9081908a832bfce71a4714 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3848d6d88190830afcf04ad12154 |
completed | March 31, 2026, 2:58 a.m. |
| PD | Predicate disambiguation | batch_69cae925ca388190ae4a01fa76e957e8 |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 4:55 p.m.