Triple
T449727
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lane |
E7099
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Melissa Lane
Melissa Lane is a political philosopher and scholar best known for her work on ancient Greek political thought and its relevance to contemporary political theory.
|
E143794
|
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: Melissa Lane | Statement: [Lane, hasNotableBearer, Melissa Lane]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Melissa Lane Context triple: [Lane, hasNotableBearer, Melissa Lane]
-
A.
Melinda Rogers
Melinda Rogers is a Canadian business executive and member of the Rogers family, known for her leadership roles within Rogers Communications.
-
B.
Marla Maples
Marla Maples is an American actress and television personality best known for her high-profile marriage to businessman and future U.S. President Donald Trump in the 1990s.
-
C.
Melissa Agretti
Melissa Agretti is a central, scheming heiress character from the 1980s American prime-time soap opera "Falcon Crest."
-
D.
Elaine Devry
Elaine Devry is an American actress known for her film and television roles in the 1950s and 1960s.
-
E.
Lisa Rogers
Lisa Rogers is a member of the Rogers family, known as the daughter of Canadian businessman and media magnate Ted Rogers.
- 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: Melissa Lane Triple: [Lane, hasNotableBearer, Melissa Lane]
Generated description
Melissa Lane is a political philosopher and scholar best known for her work on ancient Greek political thought and its relevance to contemporary political theory.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Melissa Lane Target entity description: Melissa Lane is a political philosopher and scholar best known for her work on ancient Greek political thought and its relevance to contemporary political theory.
-
A.
Melinda Rogers
Melinda Rogers is a Canadian business executive and member of the Rogers family, known for her leadership roles within Rogers Communications.
-
B.
Marla Maples
Marla Maples is an American actress and television personality best known for her high-profile marriage to businessman and future U.S. President Donald Trump in the 1990s.
-
C.
Melissa Agretti
Melissa Agretti is a central, scheming heiress character from the 1980s American prime-time soap opera "Falcon Crest."
-
D.
Elaine Devry
Elaine Devry is an American actress known for her film and television roles in the 1950s and 1960s.
-
E.
Lisa Rogers
Lisa Rogers is a member of the Rogers family, known as the daughter of Canadian businessman and media magnate Ted Rogers.
- 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_69a2e7e4676c81909ea0dbdecac0687c |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ef691cc8819091729eaac52c9457 |
completed | Feb. 28, 2026, 1:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac93942b1c819087f6fdef027f115e |
completed | March 7, 2026, 9:07 p.m. |
| NEDg | Description generation | batch_69ac94dc45d0819098a79d9e387a838e |
completed | March 7, 2026, 9:13 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ac95e2fd68819088f0ad4d5ed922e5 |
completed | March 7, 2026, 9:17 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.