Triple
T3760475
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mean Girls (2004 film) |
E82147
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Karen Smith
Karen Smith is a naive and dim-witted but sweet member of the high school clique "The Plastics" in the teen comedy film Mean Girls.
|
E498945
|
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: Karen Smith | Statement: [Mean Girls (2004 film), mainCharacter, Karen Smith]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Karen Smith Context triple: [Mean Girls (2004 film), mainCharacter, Karen Smith]
-
A.
Karen Smith
Karen Smith is a British television producer best known for creating the hit dance competition series "Strictly Come Dancing."
-
B.
Jennifer Smith
Jennifer Smith is a Bermudian politician who served as Premier and was the first woman to lead the government of Bermuda.
-
C.
Rose Smith
Rose Smith is a central daughter in the Smith family and a romantic lead in the classic 1944 MGM musical film "Meet Me in St. Louis."
-
D.
Katherine Smith
Katherine Smith is an individual known primarily as the daughter of Benjamin A. Smith II, a former U.S. Senator from Massachusetts.
-
E.
Sue Smith
Sue Smith is an Australian screenwriter known for her work on film and television, including co-writing the screenplay for "Saving Mr. Banks."
- 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: Karen Smith Triple: [Mean Girls (2004 film), mainCharacter, Karen Smith]
Generated description
Karen Smith is a naive and dim-witted but sweet member of the high school clique "The Plastics" in the teen comedy film Mean Girls.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Karen Smith Target entity description: Karen Smith is a naive and dim-witted but sweet member of the high school clique "The Plastics" in the teen comedy film Mean Girls.
-
A.
Karen Smith
Karen Smith is a British television producer best known for creating the hit dance competition series "Strictly Come Dancing."
-
B.
Jennifer Smith
Jennifer Smith is a Bermudian politician who served as Premier and was the first woman to lead the government of Bermuda.
-
C.
Rose Smith
Rose Smith is a central daughter in the Smith family and a romantic lead in the classic 1944 MGM musical film "Meet Me in St. Louis."
-
D.
Katherine Smith
Katherine Smith is an individual known primarily as the daughter of Benjamin A. Smith II, a former U.S. Senator from Massachusetts.
-
E.
Sue Smith
Sue Smith is an Australian screenwriter known for her work on film and television, including co-writing the screenplay for "Saving Mr. Banks."
- 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_69ad8b1db40081908b61ffa6b78afd4d |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adcbc3d3f48190974cec104080949f |
completed | March 8, 2026, 7:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bed8f8a8a08190b403b8c3caf20009 |
completed | March 21, 2026, 5:44 p.m. |
| NEDg | Description generation | batch_69bed9c4bd98819089c9d656379a959d |
completed | March 21, 2026, 5:47 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69beda1519348190a09e01ce4464bccc |
completed | March 21, 2026, 5:49 p.m. |
Created at: March 8, 2026, 3:35 p.m.