Triple
T33727630
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Freddie Borden |
E864186
|
entity |
| Predicate | sharesCareerWith |
P12166
|
FINISHED |
| Object | Alfred Borden |
—
|
NE NERFINISHED |
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: Alfred Borden | Statement: [Freddie Borden, sharesCareerWith, Alfred Borden]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sharesCareerWith Context triple: [Freddie Borden, sharesCareerWith, Alfred Borden]
-
A.
sharesProfessionWith
chosen
Indicates that two entities have the same profession or occupational role.
-
B.
careerWalks
Indicates the total number of bases on balls (walks) a player has received over the course of their entire career.
-
C.
careerStation
Indicates the role, position, or stage a person holds within their professional career path.
-
D.
careerAssists
Indicates the total number of assists a player has recorded over the entire span of their professional or competitive career.
-
E.
careerTriples
Indicates a relationship that links an individual to key career-related facts, such as their roles, employers, or professional milestones.
- 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_69f3498a64cc8190b4b414c67b280d93 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69ffbb1c5bf88190a0bf791213045885 |
completed | May 9, 2026, 10:54 p.m. |
| PD | Predicate disambiguation | batch_69ffba0ab0f881908f84ef81f7a1bfe8 |
completed | May 9, 2026, 10:49 p.m. |
Created at: May 1, 2026, 1:44 a.m.