Triple
T6347628
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A Love Song |
E142783
|
entity |
| Predicate | lyricist |
P1360
|
FINISHED |
| Object | Donna Weiss |
E548864
|
NE FINISHED |
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: Donna Weiss | Statement: [A Love Song, lyricist, Donna Weiss]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Donna Weiss Context triple: [A Love Song, lyricist, Donna Weiss]
-
A.
Donna Weiss
chosen
Donna Weiss is an American songwriter best known for co-writing the hit song "Bette Davis Eyes."
-
B.
Donna Moss
Donna Moss is a key fictional character on the television series "The West Wing," known for her role as Josh Lyman’s witty and capable assistant who evolves into a significant political operative.
-
C.
Nancy Schafer
Nancy Schafer is a film and television producer known for her work on independent and documentary projects.
-
D.
Debra Frisch
Debra Frisch is an American former psychology professor and blogger best known for a high-profile online harassment case involving a political commentator.
-
E.
Vicki Sirotta
Vicki Sirotta is a film producer best known for her work on the horror-thriller movie "The Prophecy."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69c008d6dcbc8190aa1c2f1fd8916b42 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c067ba2c64819094fa38bb2aeffa6c |
completed | March 22, 2026, 10:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7daf2ac9c81909a029c784cc70827 |
completed | March 28, 2026, 1:43 p.m. |
Created at: March 22, 2026, 4:31 p.m.