Triple
T20304978
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lafayette McLaws |
E505585
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Lafayette |
—
|
NE NERFINISHED |
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: [Lafayette McLaws, givenName, Lafayette]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lafayette Context triple: [Lafayette McLaws, 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
Lafayette is a small city in Boulder County, Colorado, known for its family-friendly neighborhoods, parks, and proximity to the Denver–Boulder metropolitan area.
-
D.
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.
-
E.
Place Lafayette
Place Lafayette is a central public square in the town of Villeneuve-sur-Lot in southwestern France, known as a local gathering spot and focal point of urban life.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4b8ab648190906e18538c250148 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6773ed3248190ba949ec941e8d41f |
completed | April 20, 2026, 6:58 p.m. |
Created at: April 16, 2026, 11:18 a.m.