Triple
T9154553
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Good Fight |
E219675
|
entity |
| Predicate | createdBy |
P806
|
FINISHED |
| Object | Michelle King |
E781255
|
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: Michelle King | Statement: [The Good Fight, createdBy, Michelle King]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michelle King Context triple: [The Good Fight, createdBy, Michelle King]
-
A.
Michelle King
chosen
Michelle King is an American television writer and producer best known for co-creating the acclaimed legal and political drama series "The Good Wife."
-
B.
Gail C. Murphy
Gail C. Murphy is a prominent Canadian computer scientist known for her influential research in software engineering, particularly in improving developer productivity and software evolution.
-
C.
Tina Kotek
Tina Kotek is an American politician and former state legislative leader who became the first openly lesbian governor in the United States.
-
D.
Janet Leahy
Janet Leahy is an American television writer and producer known for her work on acclaimed series such as Mad Men.
-
E.
Erinn Bartlett
Erinn Bartlett is an American actress and former beauty pageant titleholder known for supporting roles in film and television.
- 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_69ca83e25418819093c6503deeaf30de |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cca96f87ac8190b2fc6d2b2834c1b6 |
completed | April 1, 2026, 5:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0545c5bb48190b889e6e9ef0448a5 |
completed | April 3, 2026, 11:59 p.m. |
Created at: March 30, 2026, 7:20 p.m.