Triple
T12841158
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Giuliani Partners |
E307053
|
entity |
| Predicate | usesExperienceOf |
P89272
|
FINISHED |
| Object | Rudy Giuliani’s tenure as mayor of New York City |
—
|
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: Rudy Giuliani’s tenure as mayor of New York City | Statement: [Giuliani Partners, usesExperienceOf, Rudy Giuliani’s tenure as mayor of New York City]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesExperienceOf Context triple: [Giuliani Partners, usesExperienceOf, Rudy Giuliani’s tenure as mayor of New York City]
-
A.
experienceIncludes
Indicates that a particular experience encompasses, contains, or involves a specified component, activity, or element as part of it.
-
B.
typeOfExperience
Indicates that one entity specifies the category or nature of an experience associated with another entity.
-
C.
providesExperienceOf
chosen
Indicates that one entity enables or delivers the experience of another entity to someone or something.
-
D.
experienceType
Indicates the specific kind or category of experience associated with an entity or event.
-
E.
basedOnExperience
Indicates that something is determined, chosen, or formed according to prior experience or experiential knowledge.
- 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_69d7bdf52b94819096d6f0ba4ab50a98 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9714208f881908f7f8a921362909a |
completed | April 10, 2026, 9:53 p.m. |
| PD | Predicate disambiguation | batch_69d96fa08cd481909a946046ba63809f |
completed | April 10, 2026, 9:46 p.m. |
Created at: April 9, 2026, 5:35 p.m.