Triple
T1832
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Franklin D. Roosevelt |
E34
|
entity |
| Predicate | numberOfChildren |
P289
|
FINISHED |
| Object | 6 |
—
|
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: 6 | Statement: [Franklin D. Roosevelt, numberOfChildren, 6]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfChildren Context triple: [Franklin D. Roosevelt, numberOfChildren, 6]
-
A.
hasStudents
Indicates that an entity (such as a class, school, or teacher) is associated with one or more students.
-
B.
hasNumberOfMemberInstitutions
Indicates the quantitative count of member institutions associated with a given entity.
-
C.
numberOfStates
Indicates the total count of distinct states or conditions associated with an entity or system.
-
D.
parent
Indicates a relationship where one entity is the direct mother or father of another entity, from whom that other entity is descended.
-
E.
hasDivisionLevel
Indicates that one entity is associated with a specific hierarchical or organizational division level of another 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_69a22cde80848190b62c5f556b4d62ba |
completed | Feb. 27, 2026, 11:46 p.m. |
| NER | Named-entity recognition | batch_69a23344daf8819083118bbac5f46568 |
completed | Feb. 28, 2026, 12:13 a.m. |
| PD | Predicate disambiguation | batch_69a232e52e7c81909c072703e28e8c61 |
completed | Feb. 28, 2026, 12:12 a.m. |
| PDg | Predicate description generation | batch_69a233443224819097b91b150fdfbd1a |
completed | Feb. 28, 2026, 12:13 a.m. |
Created at: Feb. 27, 2026, 11:48 p.m.