Triple
T20998471
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Susan Lewis |
E517213
|
entity |
| Predicate | hasFriend |
P8712
|
FINISHED |
| Object | Doug Ross |
—
|
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: Doug Ross | Statement: [Susan Lewis, hasFriend, Doug Ross]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Doug Ross Context triple: [Susan Lewis, hasFriend, Doug Ross]
-
A.
Doug Ross
Doug Ross is a television producer and creator best known for developing the reality series "Below Deck."
-
B.
Sean McAvoy
Sean McAvoy is a fictional character appearing in the 1975 romantic drama film "Mahogany."
-
C.
Dr. Doug Ross
chosen
Dr. Doug Ross is a charismatic and rebellious pediatrician on the television medical drama "ER," known for his deep compassion for children and complex personal life.
-
D.
Dr. Eric Foreman
Dr. Eric Foreman is a neurologist and member of Dr. Gregory House’s diagnostic team on the medical drama series "House," known for his professionalism, moral convictions, and often serving as House’s ethical counterpoint.
-
E.
Marc Grossman
Marc Grossman is an editor known for his work on the publication "Vamp."
- 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_69e0b5006e2881909fc2383f841740cc |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e6fc22ca6081908bf054ddcfea9e19 |
completed | April 21, 2026, 4:25 a.m. |
Created at: April 16, 2026, 1:51 p.m.