Triple
T4946966
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Women’s October March |
E111074
|
entity |
| Predicate | hasParticipant |
P149
|
FINISHED |
| Object | Lafayette |
unclear NED1
|
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: [Women’s October March, hasParticipant, Lafayette]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lafayette Context triple: [Women’s October March, hasParticipant, Lafayette]
-
A.
Lafayette
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.
-
B.
Lafayette
Lafayette is a mid-sized city in southern Louisiana known as a cultural hub of Cajun and Creole music, food, and festivals.
-
C.
Vincennes
Vincennes is a historic commune just east of Paris, France, known for its medieval Château de Vincennes and long-standing royal connections.
-
D.
Lafayette, Louisiana
Lafayette, Louisiana is a mid-sized city in south-central Louisiana known as the heart of Cajun and Creole culture, with a vibrant music, food, and festival scene.
-
E.
Lafayette, Indiana
Lafayette, Indiana is a mid-sized city in northwestern Indiana known as a regional economic and educational hub near Purdue University.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide. chosen
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_69bd441721cc819085c7e33fe0876818 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd70abf8dc819090269d0e1ce9f871 |
completed | March 20, 2026, 4:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be77c873dc81909129644cf929ed5e |
completed | March 21, 2026, 10:49 a.m. |
Created at: March 20, 2026, 1:31 p.m.