Triple
T15613745
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Commissioner of the California Highway Patrol |
E375360
|
entity |
| Predicate | officeHeldBy |
P537
|
FINISHED |
| Object |
Joe Farrow
Joe Farrow is a law enforcement official best known for leading the California Highway Patrol as its commissioner.
|
E1167486
|
NE FINISHED |
How this triple was built (4 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: Joe Farrow | Statement: [Commissioner of the California Highway Patrol, officeHeldBy, Joe Farrow]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Joe Farrow Context triple: [Commissioner of the California Highway Patrol, officeHeldBy, Joe Farrow]
-
A.
Johnny Farrell
Johnny Farrell is a small-time gambler who becomes entangled in a dangerous love triangle and criminal intrigue in the classic 1946 film noir "Gilda."
-
B.
Efram Nugent
Efram Nugent is a fictional character appearing in the work "Girl in Landscape."
-
C.
Faron Kelley
Faron Kelley is a musician best known for his role in the American country-rock band Pure Prairie League.
-
D.
Dave Fath
Dave Fath is a Canadian businessman best known as a co-founder and owner of the professional soccer club FC Edmonton.
-
E.
Biff Elliot
Biff Elliot was an American actor best known for his tough-guy roles in film and television during the mid-20th century.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Joe Farrow Triple: [Commissioner of the California Highway Patrol, officeHeldBy, Joe Farrow]
Generated description
Joe Farrow is a law enforcement official best known for leading the California Highway Patrol as its commissioner.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Joe Farrow Target entity description: Joe Farrow is a law enforcement official best known for leading the California Highway Patrol as its commissioner.
-
A.
Johnny Farrell
Johnny Farrell is a small-time gambler who becomes entangled in a dangerous love triangle and criminal intrigue in the classic 1946 film noir "Gilda."
-
B.
Efram Nugent
Efram Nugent is a fictional character appearing in the work "Girl in Landscape."
-
C.
Faron Kelley
Faron Kelley is a musician best known for his role in the American country-rock band Pure Prairie League.
-
D.
Dave Fath
Dave Fath is a Canadian businessman best known as a co-founder and owner of the professional soccer club FC Edmonton.
-
E.
Biff Elliot
Biff Elliot was an American actor best known for his tough-guy roles in film and television during the mid-20th century.
- F. None of above. chosen
Provenance (5 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_69d85ccf2794819096cda4cbcb02d478 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04e83407c8190abbcd4b7fab0ff85 |
completed | April 16, 2026, 2:50 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff5f3913908190acdd7da62b4f521d |
completed | May 9, 2026, 4:22 p.m. |
| NEDg | Description generation | batch_69ff5ffaefb4819094468ff0008740f8 |
completed | May 9, 2026, 4:25 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff6062f0ac819081270f270ce2f057 |
completed | May 9, 2026, 4:27 p.m. |
Created at: April 10, 2026, 4:13 a.m.