Triple
T20390938
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Species |
E498081
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object | Dr. Laura Baker |
—
|
NE NERFINISHED |
How this triple was built (2 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: Dr. Laura Baker | Statement: [Species, mainCharacter, Dr. Laura Baker]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dr. Laura Baker Context triple: [Species, mainCharacter, Dr. Laura Baker]
-
A.
Dr. Laura Baker
chosen
Dr. Laura Baker is a geneticist and central protagonist in the science fiction horror film "Species II," working to understand and contain the alien hybrid threat.
-
B.
Dr. Lora Baines
Dr. Lora Baines is a fictional computer scientist and programmer from the 1982 science fiction film "Tron."
-
C.
Dr. Beth Lorenson
Dr. Beth Lorenson is a fictional psychiatrist who plays a key supporting role in the psychological thriller film "The Jacket."
-
D.
Dr. Dana Stowe
Dr. Dana Stowe is a fictional, highly driven physician and hospital administrator who serves as one of the central characters in the medical drama series "Strong Medicine."
-
E.
Dr. Susan Lewis
Dr. Susan Lewis is a central emergency physician character on the long-running medical drama series "ER," known for her compassionate care and complex personal storylines.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4a71ebc8190b153a36c738730f4 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6790f8d9c819093038f6bb6f47a92 |
completed | April 20, 2026, 7:05 p.m. |
Created at: April 16, 2026, 11:28 a.m.