Triple
T27269068
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | USS Pope |
E687995
|
entity |
| Predicate | classLength |
P165074
|
FINISHED |
| Object | approximately 314 feet |
—
|
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: approximately 314 feet | Statement: [USS Pope, classLength, approximately 314 feet]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: classLength Context triple: [USS Pope, classLength, approximately 314 feet]
-
A.
lengthClass
Indicates a classification relationship where an entity is assigned to a category based on its length.
-
B.
classStrength
Indicates the relative power, influence, or effectiveness associated with a particular class or category in a given context.
-
C.
numberInClass
Indicates that a specified entity is a member of, or belongs to, a particular class or category.
-
D.
courseLength
Indicates the duration or total length of a course, typically measured in units such as hours, weeks, or credits.
-
E.
vectorLength
Indicates the numerical magnitude or size of a vector, typically computed from its components.
- 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_69ef3557abc481908bf3c146f0f3356a |
completed | April 27, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69f65705a3048190a3728b695ba2ae65 |
completed | May 2, 2026, 7:56 p.m. |
| PD | Predicate disambiguation | batch_69f651a931748190a637e631a52bbfaa |
completed | May 2, 2026, 7:34 p.m. |
| PDg | Predicate description generation | batch_69f6562ef4e4819082ce6abd41b74dc5 |
completed | May 2, 2026, 7:53 p.m. |
Created at: April 27, 2026, 10:58 a.m.