Triple
T681023
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Blood and Sand (1922 film) |
E13180
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object |
Lila Lee
Lila Lee was a popular American silent film actress of the 1910s and 1920s, known for her girl-next-door charm and roles in major productions of the era.
|
E95059
|
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: Lila Lee | Statement: [Blood and Sand (1922 film), starring, Lila Lee]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lila Lee Context triple: [Blood and Sand (1922 film), starring, Lila Lee]
-
A.
Cecilia Peck
Cecilia Peck is an American actress, documentary filmmaker, and producer, and the daughter of legendary actor Gregory Peck.
-
B.
Eileen Loo
Eileen Loo was the wife of renowned Chinese-American architect I. M. Pei and a supportive partner throughout his celebrated career.
-
C.
Mary Lee Woods
Mary Lee Woods was a British mathematician and computer scientist who worked on early computers at Ferranti and was the mother of World Wide Web inventor Tim Berners-Lee.
-
D.
Vicky Chun
Vicky Chun is a collegiate sports administrator best known for serving as the director of athletics at Yale University.
-
E.
Gwen Bagni
Gwen Bagni was an American screenwriter known for her work in mid-20th-century film and television, including adaptations of historical and biographical stories.
- 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: Lila Lee Triple: [Blood and Sand (1922 film), starring, Lila Lee]
Generated description
Lila Lee was a popular American silent film actress of the 1910s and 1920s, known for her girl-next-door charm and roles in major productions of the era.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lila Lee Target entity description: Lila Lee was a popular American silent film actress of the 1910s and 1920s, known for her girl-next-door charm and roles in major productions of the era.
-
A.
Cecilia Peck
Cecilia Peck is an American actress, documentary filmmaker, and producer, and the daughter of legendary actor Gregory Peck.
-
B.
Eileen Loo
Eileen Loo was the wife of renowned Chinese-American architect I. M. Pei and a supportive partner throughout his celebrated career.
-
C.
Mary Lee Woods
Mary Lee Woods was a British mathematician and computer scientist who worked on early computers at Ferranti and was the mother of World Wide Web inventor Tim Berners-Lee.
-
D.
Vicky Chun
Vicky Chun is a collegiate sports administrator best known for serving as the director of athletics at Yale University.
-
E.
Gwen Bagni
Gwen Bagni was an American screenwriter known for her work in mid-20th-century film and television, including adaptations of historical and biographical stories.
- 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_69a4933d3bf88190972041cd8cf143b9 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4a06e294c8190873116a3253e04f9 |
completed | March 1, 2026, 8:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a68918e87c819085df25ee862ba836 |
completed | March 3, 2026, 7:09 a.m. |
| NEDg | Description generation | batch_69a68d097d088190aa19d2857c6419c2 |
completed | March 3, 2026, 7:26 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a6ce6e692081908a49b12729f59a84 |
completed | March 3, 2026, 12:05 p.m. |
Created at: March 1, 2026, 7:36 p.m.