Triple
T29271885
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fannie Mae Headquarters, Washington, D.C. |
E742139
|
entity |
| Predicate | hasPrimaryOccupantIndustry |
P13077
|
FINISHED |
| Object | mortgage finance |
—
|
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: mortgage finance | Statement: [Fannie Mae Headquarters, Washington, D.C., hasPrimaryOccupantIndustry, mortgage finance]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPrimaryOccupantIndustry Context triple: [Fannie Mae Headquarters, Washington, D.C., hasPrimaryOccupantIndustry, mortgage finance]
-
A.
occupantIndustry
Indicates the industry or sector in which an occupant (such as a tenant or user of a space) operates.
-
B.
hasPrincipalIndustry
chosen
Indicates that an entity’s main or primary industry of operation is the specified industry.
-
C.
hasIndustrialEmployer
Indicates that an entity is employed by, or has an employment relationship with, an industrial organization or company.
-
D.
dominantOccupation
Indicates the primary type of work or profession that most characterizes an entity’s economic or labor activity.
-
E.
hasMajorEmployerType
Indicates the type or category of major employer associated with an entity.
- 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_69f0912124d48190a046642b69407f4c |
completed | April 28, 2026, 10:51 a.m. |
| NER | Named-entity recognition | batch_69ff63225b6481909217ad11b4f7d3ba |
completed | May 9, 2026, 4:38 p.m. |
| PD | Predicate disambiguation | batch_69ff60e0882c819085d097010db43ee0 |
completed | May 9, 2026, 4:29 p.m. |
Created at: April 28, 2026, 12:48 p.m.