Triple
T14656264
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Road Dogs |
E344116
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Dawn Navarro
Dawn Navarro is a manipulative and cunning psychic and con artist who plays a central role in Elmore Leonard’s crime novel "Road Dogs."
|
E1132808
|
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: Dawn Navarro | Statement: [Road Dogs, mainCharacter, Dawn Navarro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dawn Navarro Context triple: [Road Dogs, mainCharacter, Dawn Navarro]
-
A.
Michelle Navarro
Michelle Navarro is an individual notable enough to be recognized as a prominent bearer of the Navarro surname.
-
B.
Melissa Navia
Melissa Navia is an American actress best known for her role as Lt. Erica Ortegas on the television series "Star Trek: Strange New Worlds."
-
C.
Gina Cuevas
Gina Cuevas is a fictional character appearing in the American medical drama television series "Nurses."
-
D.
Nadine Velazquez
Nadine Velazquez is an American actress and model best known for her roles in the sitcom "My Name Is Earl" and the film "Flight."
-
E.
Laurel Castillo
Laurel Castillo is a driven and morally conflicted law student who becomes deeply entangled in the central murder conspiracies on the television series "How to Get Away with Murder."
- 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: Dawn Navarro Triple: [Road Dogs, mainCharacter, Dawn Navarro]
Generated description
Dawn Navarro is a manipulative and cunning psychic and con artist who plays a central role in Elmore Leonard’s crime novel "Road Dogs."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dawn Navarro Target entity description: Dawn Navarro is a manipulative and cunning psychic and con artist who plays a central role in Elmore Leonard’s crime novel "Road Dogs."
-
A.
Michelle Navarro
Michelle Navarro is an individual notable enough to be recognized as a prominent bearer of the Navarro surname.
-
B.
Melissa Navia
Melissa Navia is an American actress best known for her role as Lt. Erica Ortegas on the television series "Star Trek: Strange New Worlds."
-
C.
Gina Cuevas
Gina Cuevas is a fictional character appearing in the American medical drama television series "Nurses."
-
D.
Nadine Velazquez
Nadine Velazquez is an American actress and model best known for her roles in the sitcom "My Name Is Earl" and the film "Flight."
-
E.
Laurel Castillo
Laurel Castillo is a driven and morally conflicted law student who becomes deeply entangled in the central murder conspiracies on the television series "How to Get Away with Murder."
- 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_69d822e1a2cc81908e5bb93cf61ce3cc |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb51a562c819098971447db4b29f7 |
completed | April 14, 2026, 9:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe9dba87c481908084c3cba5df3fcd |
completed | May 9, 2026, 2:36 a.m. |
| NEDg | Description generation | batch_69fe9e41de7c8190a2c2ce525d04fb49 |
completed | May 9, 2026, 2:38 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fe9edd6fac8190907cdadb0de02d63 |
completed | May 9, 2026, 2:41 a.m. |
Created at: April 10, 2026, 1:27 a.m.