Triple
T4397153
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oregon Beach Bill |
E99518
|
entity |
| Predicate | sponsor |
P67
|
FINISHED |
| Object |
Don McCall
Don McCall was an Oregon state legislator best known for championing landmark public access protections for the state’s beaches.
|
E435991
|
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: Don McCall | Statement: [Oregon Beach Bill, sponsor, Don McCall]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Don McCall Context triple: [Oregon Beach Bill, sponsor, Don McCall]
-
A.
Ernie McCracken
Ernie McCracken is the flamboyantly villainous professional bowler and main antagonist portrayed by Bill Murray in the comedy film "Kingpin."
-
B.
Tom Hall
Tom Hall is an American game designer best known as one of the original co-founders and creative leads behind the pioneering video game company id Software.
-
C.
Tom Bell
Tom Bell was an American football official best known for serving as the referee in Super Bowl III.
-
D.
Donald McAlpine
Donald McAlpine is an acclaimed Australian cinematographer known for his visually distinctive work on numerous prominent films across several decades.
-
E.
Dan Moore
Dan Moore is a fictional character appearing in the work "Cane."
- 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: Don McCall Triple: [Oregon Beach Bill, sponsor, Don McCall]
Generated description
Don McCall was an Oregon state legislator best known for championing landmark public access protections for the state’s beaches.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Don McCall Target entity description: Don McCall was an Oregon state legislator best known for championing landmark public access protections for the state’s beaches.
-
A.
Ernie McCracken
Ernie McCracken is the flamboyantly villainous professional bowler and main antagonist portrayed by Bill Murray in the comedy film "Kingpin."
-
B.
Tom Hall
Tom Hall is an American game designer best known as one of the original co-founders and creative leads behind the pioneering video game company id Software.
-
C.
Tom Bell
Tom Bell was an American football official best known for serving as the referee in Super Bowl III.
-
D.
Donald McAlpine
Donald McAlpine is an acclaimed Australian cinematographer known for his visually distinctive work on numerous prominent films across several decades.
-
E.
Dan Moore
Dan Moore is a fictional character appearing in the work "Cane."
- 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_69b345506b408190b0e3dee616738a7d |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b352aca86c8190b5af7e6600072066 |
completed | March 12, 2026, 11:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5e53ae7bc8190b216319e522b11c6 |
completed | March 14, 2026, 10:46 p.m. |
| NEDg | Description generation | batch_69b5e5f8b9bc8190abb35710e2ddbe5e |
completed | March 14, 2026, 10:49 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b5e62af694819086b3eddb71f591d2 |
completed | March 14, 2026, 10:50 p.m. |
Created at: March 12, 2026, 11:20 p.m.