Triple
T8535487
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1890 United States Census |
E202066
|
entity |
| Predicate | dataLossExtent |
P24423
|
FINISHED |
| Object | most population schedules destroyed |
—
|
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: most population schedules destroyed | Statement: [1890 United States Census, dataLossExtent, most population schedules destroyed]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: dataLossExtent Context triple: [1890 United States Census, dataLossExtent, most population schedules destroyed]
-
A.
causedLossOf
Indicates that one entity brought about or was responsible for another entity experiencing a loss.
-
B.
dateOfLoss
Indicates the specific date on which a loss event (such as damage, theft, or other covered incident) occurred.
-
C.
dataRollover
Indicates that unused data from a previous period is carried over for use in a subsequent period.
-
D.
significantLoss
chosen
Indicates that an entity has experienced a major or substantial decrease in value, quantity, or status beyond a normal or minor loss.
-
E.
hasPartiallyLostUseOf
Indicates that an entity has experienced a reduction, but not a complete loss, in the functional use of another 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_69ca832355b08190b8b6a4ab4a4a3554 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe6a295c88190a432a060ee73f04e |
completed | March 31, 2026, 3:22 p.m. |
| PD | Predicate disambiguation | batch_69cbd111bf988190be98c92a607c6456 |
completed | March 31, 2026, 1:50 p.m. |
Created at: March 30, 2026, 6:17 p.m.