Triple
T12223081
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Betty Lou Keim |
E291269
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Texas Lady
Texas Lady is a 1955 Western film featuring Betty Lou Keim in a supporting role alongside Claudette Colbert.
|
E968803
|
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: Texas Lady | Statement: [Betty Lou Keim, notableWork, Texas Lady]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Texas Lady Context triple: [Betty Lou Keim, notableWork, Texas Lady]
-
A.
Lone Stars
The Lone Stars is the nickname of the Liberia national football team, which represents Liberia in international soccer competitions.
-
B.
Tulsa Queen
"Tulsa Queen" is a song featured on the album "Luxury Liner" by country artist Emmylou Harris.
-
C.
The Eyes of Texas
"The Eyes of Texas" is a traditional anthem closely associated with the University of Texas at Austin, widely recognized as a central part of Longhorns sports and school spirit.
-
D.
Tia Texada
Tia Texada is an American actress and voice artist known for her roles in film and television, including a notable appearance in the early-2000s music drama "Glitter."
-
E.
Loretta
Loretta is a feminine given name of Latin origin, often associated with the laurel tree and borne by various notable figures.
- 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: Texas Lady Triple: [Betty Lou Keim, notableWork, Texas Lady]
Generated description
Texas Lady is a 1955 Western film featuring Betty Lou Keim in a supporting role alongside Claudette Colbert.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Texas Lady Target entity description: Texas Lady is a 1955 Western film featuring Betty Lou Keim in a supporting role alongside Claudette Colbert.
-
A.
Lone Stars
The Lone Stars is the nickname of the Liberia national football team, which represents Liberia in international soccer competitions.
-
B.
Tulsa Queen
"Tulsa Queen" is a song featured on the album "Luxury Liner" by country artist Emmylou Harris.
-
C.
The Eyes of Texas
"The Eyes of Texas" is a traditional anthem closely associated with the University of Texas at Austin, widely recognized as a central part of Longhorns sports and school spirit.
-
D.
Tia Texada
Tia Texada is an American actress and voice artist known for her roles in film and television, including a notable appearance in the early-2000s music drama "Glitter."
-
E.
Loretta
Loretta is a feminine given name of Latin origin, often associated with the laurel tree and borne by various notable figures.
- 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_69d6ab668acc8190963ba424049d6aee |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d91ca11f788190bad2efb6c83ffccb |
completed | April 10, 2026, 3:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f60aa6d3d481909852a6f2f90d7a41 |
completed | May 2, 2026, 2:31 p.m. |
| NEDg | Description generation | batch_69f60c06e4c08190985114da9317e8fd |
completed | May 2, 2026, 2:36 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f60c99de9881909ef87b878ac346fc |
completed | May 2, 2026, 2:39 p.m. |
Created at: April 8, 2026, 9:51 p.m.