Triple
T2884864
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | First Lady |
E59480
|
entity |
| Predicate | mayHaveOffice |
P19057
|
FINISHED |
| Object | staffed support office |
—
|
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: staffed support office | Statement: [First Lady, mayHaveOffice, staffed support office]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mayHaveOffice Context triple: [First Lady, mayHaveOffice, staffed support office]
-
A.
hasOffice
Indicates that an entity possesses or maintains an office at a particular location or within a specific organization.
-
B.
hasAssociatedOffice
chosen
Indicates that an entity is linked to or connected with a particular office in an official or functional capacity.
-
C.
officeHolderMayBe
Indicates that a specified person is permitted or eligible to hold a particular office or position.
-
D.
hasOfficeType
Indicates that an entity’s office is classified as a specific type or category of office.
-
E.
memberHoldsOffice
Indicates that a member occupies or serves in a specific official position or office within an organization or governing body.
- 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_69ab4ac739188190a112f42a5a69c951 |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abe04476588190b0db0880e14c79b5 |
completed | March 7, 2026, 8:22 a.m. |
| PD | Predicate disambiguation | batch_69abdd15cbf08190bf7fea5ea516848a |
completed | March 7, 2026, 8:08 a.m. |
Created at: March 6, 2026, 10:03 p.m.