Triple
T34103987
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | University of American Samoa |
E874648
|
entity |
| Predicate | usedAsPropFor |
P191805
|
FINISHED |
| Object | diploma in Saul Goodman’s office |
—
|
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: diploma in Saul Goodman’s office | Statement: [University of American Samoa, usedAsPropFor, diploma in Saul Goodman’s office]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedAsPropFor Context triple: [University of American Samoa, usedAsPropFor, diploma in Saul Goodman’s office]
-
A.
propUsed
Indicates that a particular property or attribute is utilized or applied in a given context or situation.
-
B.
usesProp
Indicates that one entity employs, utilizes, or makes use of a particular property, resource, or object in performing an action or fulfilling a function.
-
C.
isUsedAs
Indicates that one entity serves a particular function, role, or purpose as another entity.
-
D.
appearsAsProp
chosen
Indicates that one entity is used or presented as a prop in relation to another entity or context.
-
E.
usedInType
Indicates that something serves as a component, element, or example within a particular type or category.
- 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_69f349a80d4481908527317d43f5c579 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69fe189fec148190aeef51b417ba15b0 |
completed | May 8, 2026, 5:08 p.m. |
| PD | Predicate disambiguation | batch_69fe17285b0881908de7569d8dbd20bd |
completed | May 8, 2026, 5:02 p.m. |
Created at: May 1, 2026, 1:53 a.m.