Triple
T24734382
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Federal Land Bank Association |
E618370
|
entity |
| Predicate | creditFocus |
P157214
|
FINISHED |
| Object | secured real estate lending |
—
|
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: secured real estate lending | Statement: [Federal Land Bank Association, creditFocus, secured real estate lending]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: creditFocus Context triple: [Federal Land Bank Association, creditFocus, secured real estate lending]
-
A.
creditFunction
Indicates a financial role or operation through which an entity extends, manages, or utilizes credit within an economic or transactional context.
-
B.
hasBureau
Indicates that an entity is associated with or possesses a specific bureau, such as an office, department, or administrative unit.
-
C.
creditQualityInfluencedBy
Indicates that the credit quality of one entity is affected or determined by another factor or entity.
-
D.
hasCreditRating
Indicates that an entity is assigned a formal assessment of its creditworthiness, typically expressed as a credit score or rating.
-
E.
creditBuildingMethod
Indicates the method or approach used to build or improve credit.
- 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_69e2fab772608190b74163751047ff50 |
completed | April 18, 2026, 3:29 a.m. |
| NER | Named-entity recognition | batch_69f422aee0408190899efe7e24ef2b40 |
completed | May 1, 2026, 3:49 a.m. |
| PD | Predicate disambiguation | batch_69f420e92cc88190a803aecdae78a051 |
completed | May 1, 2026, 3:41 a.m. |
| PDg | Predicate description generation | batch_69f422add8508190a76e56cfa756eeb8 |
completed | May 1, 2026, 3:49 a.m. |
Created at: April 18, 2026, 4:03 a.m.