Triple
T2995680
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jockey |
E81058
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object |
Parker Laramie
Parker Laramie is an editor known for working on the film "Jockey."
|
E315762
|
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: Parker Laramie | Statement: [Jockey, editedBy, Parker Laramie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Parker Laramie Context triple: [Jockey, editedBy, Parker Laramie]
-
A.
Parker Harris
Parker Harris is a technology entrepreneur best known as a co-founder and longtime chief technology leader of the cloud-based software company Salesforce.
-
B.
Brennan Chapman
Brennan Chapman is a cinematographer known for his work on the animated feature film "The Super Mario Bros. Movie."
-
C.
Sage Kotsenburg
Sage Kotsenburg is an American snowboarder best known for winning the first-ever Olympic gold medal in men's slopestyle at the 2014 Winter Olympics in Sochi.
-
D.
Brooks Hatlen
Brooks Hatlen is an elderly prison librarian in "The Shawshank Redemption" whose tragic struggle to adapt to life outside prison highlights the film’s themes of institutionalization and hopelessness.
-
E.
Garrett Camp
Garrett Camp is a Canadian entrepreneur and investor best known as the co-founder of Uber and the founder of the discovery platform StumbleUpon.
- 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: Parker Laramie Triple: [Jockey, editedBy, Parker Laramie]
Generated description
Parker Laramie is an editor known for working on the film "Jockey."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Parker Laramie Target entity description: Parker Laramie is an editor known for working on the film "Jockey."
-
A.
Parker Harris
Parker Harris is a technology entrepreneur best known as a co-founder and longtime chief technology leader of the cloud-based software company Salesforce.
-
B.
Brennan Chapman
Brennan Chapman is a cinematographer known for his work on the animated feature film "The Super Mario Bros. Movie."
-
C.
Sage Kotsenburg
Sage Kotsenburg is an American snowboarder best known for winning the first-ever Olympic gold medal in men's slopestyle at the 2014 Winter Olympics in Sochi.
-
D.
Brooks Hatlen
Brooks Hatlen is an elderly prison librarian in "The Shawshank Redemption" whose tragic struggle to adapt to life outside prison highlights the film’s themes of institutionalization and hopelessness.
-
E.
Garrett Camp
Garrett Camp is a Canadian entrepreneur and investor best known as the co-founder of Uber and the founder of the discovery platform StumbleUpon.
- 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_69ad8b187fc8819085914d3c9ea3142d |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad99f481688190ae8cd1e057f9dfc7 |
completed | March 8, 2026, 3:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b10908cfe48190bf244d5a3dbc958b |
completed | March 11, 2026, 6:17 a.m. |
| NEDg | Description generation | batch_69b10ab5e3488190b2d8c98dd296cbe5 |
completed | March 11, 2026, 6:24 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b10b1664f081909b521ee8dd5954f9 |
completed | March 11, 2026, 6:26 a.m. |
Created at: March 8, 2026, 2:59 p.m.