Triple
T31490958
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1860 United States census |
E803401
|
entity |
| Predicate | hasNumberOfQuestions |
P15109
|
FINISHED |
| Object | 13 |
—
|
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: 13 | Statement: [1860 United States census, hasNumberOfQuestions, 13]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfQuestions Context triple: [1860 United States census, hasNumberOfQuestions, 13]
-
A.
numberOfQuestions
chosen
Indicates the total count of questions associated with or contained in a given entity or context.
-
B.
hasNumberOfPossibleAnswers
Indicates the quantity of distinct answers or response options that are possible for a given item, question, or situation.
-
C.
numberOfProblems
Indicates the quantity or count of problems associated with a given entity or situation.
-
D.
hasOpenQuestions
Indicates that there are unresolved or unanswered issues, problems, or inquiries associated with the referenced entity or context.
-
E.
hasKeyQuestion
Indicates that one entity possesses or is associated with a primary or central question relevant to 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_69f348ca04508190ba9379b5329dfd75 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69fdb31800508190beec15adb9bbd292 |
completed | May 8, 2026, 9:55 a.m. |
| PD | Predicate disambiguation | batch_69fdb19c381c8190bafb2f565da097f1 |
completed | May 8, 2026, 9:49 a.m. |
Created at: April 30, 2026, 9:38 p.m.