Triple
T21428
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United States Capitol |
E425
|
entity |
| Predicate | firstOccupied |
P1644
|
FINISHED |
| Object | 1800 |
—
|
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: 1800 | Statement: [United States Capitol, firstOccupied, 1800]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: firstOccupied Context triple: [United States Capitol, firstOccupied, 1800]
-
A.
firstOccupiedBy
Indicates which entity was the initial or earliest known occupant of a given place, position, or resource.
-
B.
primaryStation
Indicates that one station is designated as the main or principal station associated with another entity or within a given context.
-
C.
firstInOfficeTo
Indicates that one entity was the earliest or first to hold a particular office or position in relation to another entity or context.
-
D.
firstAwarded
Indicates the time or occasion when an award, honor, or recognition was given for the very first time.
-
E.
usedAt
Indicates that something is employed, applied, or utilized at a particular place, time, or context.
- 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_69a243b4ac2c8190b93c303df797b7b2 |
completed | Feb. 28, 2026, 1:24 a.m. |
| NER | Named-entity recognition | batch_69a246e94ca881908f7a7d2c0b293033 |
completed | Feb. 28, 2026, 1:37 a.m. |
| PD | Predicate disambiguation | batch_69a24654724481909ba14b7f68d2a472 |
completed | Feb. 28, 2026, 1:35 a.m. |
| PDg | Predicate description generation | batch_69a246e7fac481909b0c500d4500650e |
completed | Feb. 28, 2026, 1:37 a.m. |
Created at: Feb. 28, 2026, 1:34 a.m.