Triple
T917990
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lerner Enterprises |
E19812
|
entity |
| Predicate | isMajorCompanyIn |
P22346
|
FINISHED |
| Object | Washington, D.C. real estate market |
—
|
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: Washington, D.C. real estate market | Statement: [Lerner Enterprises, isMajorCompanyIn, Washington, D.C. real estate market]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isMajorCompanyIn Context triple: [Lerner Enterprises, isMajorCompanyIn, Washington, D.C. real estate market]
-
A.
hasMajorOrganization
Indicates that an entity is associated with or primarily represented by a major organization.
-
B.
hasParentCompany
Indicates that one company is owned or controlled by another company that serves as its parent organization.
-
C.
memberCompany
Indicates that a company is formally part of, or affiliated as a member with, a larger organization, group, or association.
-
D.
hasMajorEmployer
Indicates that an entity has a primary or most significant employer with which it is chiefly affiliated for work or occupation.
-
E.
hasNumberOfCompanies
Indicates the quantitative relationship specifying how many companies are associated with a given entity.
- 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_69a493a099788190a696d9d8408cbaf4 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b388f0bc8190a087222636135ba5 |
completed | March 1, 2026, 9:45 p.m. |
| PD | Predicate disambiguation | batch_69a4b2944ff88190a260be5355132ba5 |
completed | March 1, 2026, 9:41 p.m. |
| PDg | Predicate description generation | batch_69a4b385176081909e3e8c3f647c1fd4 |
completed | March 1, 2026, 9:45 p.m. |
Created at: March 1, 2026, 7:40 p.m.