Triple
T9642621
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LaVonne Griffin-Valade |
E233110
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
LaVonne
LaVonne is a feminine given name of French origin, derived from "Lavonne" and related to "Yvonne," meaning "yew" or "archer."
|
E812263
|
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: LaVonne | Statement: [LaVonne Griffin-Valade, givenName, LaVonne]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: LaVonne Context triple: [LaVonne Griffin-Valade, givenName, LaVonne]
-
A.
Lola Lane
Lola Lane was an American film actress best known as one of the Lane Sisters, who appeared in numerous Hollywood productions during the 1930s and 1940s.
-
B.
Nanci
Nanci is a feminine given name most notably associated with the late American folk and country singer-songwriter Nanci Griffith.
-
C.
Vonetta
Vonetta is a feminine given name most notably borne by American bobsledder and Olympic gold medalist Vonetta Flowers.
-
D.
Yvonne Fair
Yvonne Fair was an American soul and R&B singer associated with the Motown label, known for her powerful vocals and dynamic stage presence in the 1960s and 1970s.
-
E.
Darlene
Darlene is a fictional character portrayed by actress Dominique Fishback, known from her work in film and television dramas.
- 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: LaVonne Triple: [LaVonne Griffin-Valade, givenName, LaVonne]
Generated description
LaVonne is a feminine given name of French origin, derived from "Lavonne" and related to "Yvonne," meaning "yew" or "archer."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: LaVonne Target entity description: LaVonne is a feminine given name of French origin, derived from "Lavonne" and related to "Yvonne," meaning "yew" or "archer."
-
A.
Lola Lane
Lola Lane was an American film actress best known as one of the Lane Sisters, who appeared in numerous Hollywood productions during the 1930s and 1940s.
-
B.
Nanci
Nanci is a feminine given name most notably associated with the late American folk and country singer-songwriter Nanci Griffith.
-
C.
Vonetta
Vonetta is a feminine given name most notably borne by American bobsledder and Olympic gold medalist Vonetta Flowers.
-
D.
Yvonne Fair
Yvonne Fair was an American soul and R&B singer associated with the Motown label, known for her powerful vocals and dynamic stage presence in the 1960s and 1970s.
-
E.
Darlene
Darlene is a fictional character portrayed by actress Dominique Fishback, known from her work in film and television dramas.
- 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_69ca848a5a908190aad251f4137b0c3a |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9b7cda388190a38f96d78085f404 |
completed | April 1, 2026, 10:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d18252c1cc81908d33c9c55d645fbf |
completed | April 4, 2026, 9:27 p.m. |
| NEDg | Description generation | batch_69d18324756c819089eb7edc107ed8b2 |
completed | April 4, 2026, 9:31 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d1856b88908190a787765e1d8f001f |
completed | April 4, 2026, 9:40 p.m. |
Created at: March 30, 2026, 8:12 p.m.