Triple
T9385933
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Tower Chronicles |
E225904
|
entity |
| Predicate | colorist |
P36862
|
FINISHED |
| Object |
Ryan Brown
Ryan Brown is a comic book colorist known for his work on the supernatural action series *The Tower Chronicles*.
|
E796044
|
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: Ryan Brown | Statement: [The Tower Chronicles, colorist, Ryan Brown]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ryan Brown Context triple: [The Tower Chronicles, colorist, Ryan Brown]
-
A.
Ryan Brown
Ryan Brown is a musician best known for his past role as a member of the rock band Papa Roach.
-
B.
Ryan Brown
Ryan Brown is a film editor known for his work on the movie "Horse Girl."
-
C.
Brad Brown
Brad Brown is the young protagonist who battles the carnivorous alien creatures in the horror-comedy film series "Critters," including "Critters 2: The Main Course."
-
D.
Donald Brown
Donald Brown is a fictional character from the novel and film "National Velvet," known as one of Velvet Brown’s brothers in the Brown family.
-
E.
Warrick Brown
Warrick Brown is a fictional crime scene investigator and forensic analyst on the television series "CSI: Crime Scene Investigation."
- 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: Ryan Brown Triple: [The Tower Chronicles, colorist, Ryan Brown]
Generated description
Ryan Brown is a comic book colorist known for his work on the supernatural action series *The Tower Chronicles*.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ryan Brown Target entity description: Ryan Brown is a comic book colorist known for his work on the supernatural action series *The Tower Chronicles*.
-
A.
Ryan Brown
Ryan Brown is a film editor known for his work on the movie "Horse Girl."
-
B.
Ryan Brown
Ryan Brown is a musician best known for his past role as a member of the rock band Papa Roach.
-
C.
Brad Brown
Brad Brown is the young protagonist who battles the carnivorous alien creatures in the horror-comedy film series "Critters," including "Critters 2: The Main Course."
-
D.
Donald Brown
Donald Brown is a fictional character from the novel and film "National Velvet," known as one of Velvet Brown’s brothers in the Brown family.
-
E.
Warrick Brown
Warrick Brown is a fictional crime scene investigator and forensic analyst on the television series "CSI: Crime Scene Investigation."
- 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_69ca842e9dcc8190a264119e683cfe04 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd50d3964c8190b0353f56df755db8 |
completed | April 1, 2026, 5:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d100eb108c8190add5bacfea1f800a |
completed | April 4, 2026, 12:15 p.m. |
| NEDg | Description generation | batch_69d1017a36e0819091bd6d7bc75d1a97 |
completed | April 4, 2026, 12:18 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d10270f9948190bbf937089f88bacf |
completed | April 4, 2026, 12:22 p.m. |
Created at: March 30, 2026, 7:44 p.m.