Triple
T30430795
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Della Frye |
E774157
|
entity |
| Predicate | careerDevelopment |
P169428
|
FINISHED |
| Object | transitions from blogger to print reporter |
—
|
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: transitions from blogger to print reporter | Statement: [Della Frye, careerDevelopment, transitions from blogger to print reporter]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: careerDevelopment Context triple: [Della Frye, careerDevelopment, transitions from blogger to print reporter]
-
A.
careerTackles
Indicates the total number of tackles a player has made over the course of their entire career.
-
B.
careerAssists
Indicates the total number of assists a player has recorded over the entire span of their professional or competitive career.
-
C.
careerStart
Indicates the point in time when an entity begins its professional career or main occupational activity.
-
D.
businessCareer
Indicates a relationship where an entity’s professional life, roles, or progression is specifically within the field of business or commerce.
-
E.
earlyCareerCoach
Indicates that one entity serves as a coach or mentor to another during the early stage of that other entity’s career.
- 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_69f22492d2a88190995ce8745d9becaa |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f6866e2fcc819090808635c0818ed9 |
completed | May 2, 2026, 11:19 p.m. |
| PD | Predicate disambiguation | batch_69f678d2196c8190b9d0d2fcd47cc539 |
completed | May 2, 2026, 10:21 p.m. |
| PDg | Predicate description generation | batch_69f67d31cc60819084f64bd056e1ea4d |
completed | May 2, 2026, 10:39 p.m. |
Created at: April 29, 2026, 8:06 p.m.