Triple
T11513662
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maynard Dixon |
E272976
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Lafayette |
E87859
|
NE FINISHED |
How this triple was built (2 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: Lafayette | Statement: [Maynard Dixon, givenName, Lafayette]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lafayette Context triple: [Maynard Dixon, givenName, Lafayette]
-
A.
Lafayette
Lafayette is a mid-sized city in southern Louisiana known as a cultural hub of Cajun and Creole music, food, and festivals.
-
B.
Lafayette
Lafayette is a mid-sized city in northwestern Indiana known for its proximity to Purdue University and its role as a regional economic and cultural center.
-
C.
Lafayette
chosen
Lafayette was a French aristocrat and military officer who became a key general in the American Revolutionary War and a symbol of Franco-American alliance.
-
D.
Fort Louis
Fort Louis is a historic coastal fortification in the Caribbean archipelago of Les Saintes, later renamed Fort Napoléon and now known for its panoramic views and museum.
-
E.
Vincennes
Vincennes is a historic commune just east of Paris, France, known for its medieval Château de Vincennes and long-standing royal connections.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d6aae2c3748190bed2ea50dfb160dc |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d86db8bf9c8190820c289e6b0c3873 |
completed | April 10, 2026, 3:25 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e625143a608190a1119b30c08df0fd |
completed | April 20, 2026, 1:07 p.m. |
Created at: April 8, 2026, 9:36 p.m.