Triple
T2995583
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Extract |
E81056
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Joel Reynolds
Joel Reynolds is a fictional protagonist whose story centers on his personal experiences and development within the narrative from which he is drawn.
|
E330388
|
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: Joel Reynolds | Statement: [Extract, mainCharacter, Joel Reynolds]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Joel Reynolds Context triple: [Extract, mainCharacter, Joel Reynolds]
-
A.
Joel Parker
Joel Parker is a name shared by several notable individuals, including historical American politicians and jurists.
-
B.
Joel Cox
Joel Cox is an American film editor best known for his long-time collaboration with director Clint Eastwood on numerous acclaimed movies.
-
C.
Joel Aldrich Matteson
Joel Aldrich Matteson was a 19th-century American politician who served as governor of Illinois.
-
D.
Joel Allen
Joel Allen is an American actor best known for his starring role in the television adaptation of the horror franchise "The Purge."
-
E.
Scott Reed
Scott Reed is a computer scientist and machine learning researcher known for his work on deep learning and generative models.
- 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: Joel Reynolds Triple: [Extract, mainCharacter, Joel Reynolds]
Generated description
Joel Reynolds is a fictional protagonist whose story centers on his personal experiences and development within the narrative from which he is drawn.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Joel Reynolds Target entity description: Joel Reynolds is a fictional protagonist whose story centers on his personal experiences and development within the narrative from which he is drawn.
-
A.
Joel Parker
Joel Parker is a name shared by several notable individuals, including historical American politicians and jurists.
-
B.
Joel Cox
Joel Cox is an American film editor best known for his long-time collaboration with director Clint Eastwood on numerous acclaimed movies.
-
C.
Joel Aldrich Matteson
Joel Aldrich Matteson was a 19th-century American politician who served as governor of Illinois.
-
D.
Joel Allen
Joel Allen is an American actor best known for his starring role in the television adaptation of the horror franchise "The Purge."
-
E.
Scott Reed
Scott Reed is a computer scientist and machine learning researcher known for his work on deep learning and generative models.
- 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_69ad99f2e5888190b3346012e2578dab |
completed | March 8, 2026, 3:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b20f38a1548190bdddf65853632db1 |
completed | March 12, 2026, 12:56 a.m. |
| NEDg | Description generation | batch_69b2102e35b08190ad9ca397f0c937da |
completed | March 12, 2026, 1 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b21458b07081909d75886e0d9f88e9 |
completed | March 12, 2026, 1:18 a.m. |
Created at: March 8, 2026, 2:59 p.m.