Triple
T1982441
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La La Land |
E43057
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Mia Dolan
Mia Dolan is an aspiring actress in Los Angeles and one of the two central protagonists of the musical film "La La Land."
|
E263928
|
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: Mia Dolan | Statement: [La La Land, mainCharacter, Mia Dolan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mia Dolan Context triple: [La La Land, mainCharacter, Mia Dolan]
-
A.
Molly Stark
Molly Stark was the wife of American Revolutionary War General John Stark, remembered in part through his famous battle cry invoking her name at the Battle of Bennington.
-
B.
Sophia Hitchens
Sophia Hitchens is the daughter of the late British-American author and polemicist Christopher Hitchens.
-
C.
Beth Nolan
Beth Nolan is an American lawyer and legal scholar who served as White House Counsel to President Bill Clinton.
-
D.
Zoe Murphy
Zoe Murphy is a central character in the musical "Dear Evan Hansen," known as Connor Murphy’s sister and Evan’s love interest, whose story explores grief, family dynamics, and the search for connection.
-
E.
Carley Knox
Carley Knox is a sports executive best known for her leadership role in the WNBA’s Minnesota Lynx organization.
- 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: Mia Dolan Triple: [La La Land, mainCharacter, Mia Dolan]
Generated description
Mia Dolan is an aspiring actress in Los Angeles and one of the two central protagonists of the musical film "La La Land."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mia Dolan Target entity description: Mia Dolan is an aspiring actress in Los Angeles and one of the two central protagonists of the musical film "La La Land."
-
A.
Molly Stark
Molly Stark was the wife of American Revolutionary War General John Stark, remembered in part through his famous battle cry invoking her name at the Battle of Bennington.
-
B.
Sophia Hitchens
Sophia Hitchens is the daughter of the late British-American author and polemicist Christopher Hitchens.
-
C.
Beth Nolan
Beth Nolan is an American lawyer and legal scholar who served as White House Counsel to President Bill Clinton.
-
D.
Zoe Murphy
Zoe Murphy is a central character in the musical "Dear Evan Hansen," known as Connor Murphy’s sister and Evan’s love interest, whose story explores grief, family dynamics, and the search for connection.
-
E.
Carley Knox
Carley Knox is a sports executive best known for her leadership role in the WNBA’s Minnesota Lynx organization.
- 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_69a88713ddc88190a969715658ebe7a8 |
completed | March 4, 2026, 7:25 p.m. |
| NER | Named-entity recognition | batch_69abb81f5dac8190b5223fe2d59ee0d4 |
completed | March 7, 2026, 5:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69aeb3abc8cc819086e7b640d5231641 |
completed | March 9, 2026, 11:48 a.m. |
| NEDg | Description generation | batch_69aeb5cf422c8190938b1c113270db58 |
completed | March 9, 2026, 11:58 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69aeb6327ad08190926eb12ffe8f317c |
completed | March 9, 2026, 11:59 a.m. |
Created at: March 4, 2026, 7:37 p.m.