Triple
T18816846
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maranello |
E460157
|
entity |
| Predicate | twinTown |
P1072
|
FINISHED |
| Object | Vincennes |
—
|
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: Vincennes | Statement: [Maranello, twinTown, Vincennes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vincennes Context triple: [Maranello, twinTown, Vincennes]
-
A.
Vincennes
chosen
Vincennes is a historic commune just east of Paris, France, known for its medieval Château de Vincennes and long-standing royal connections.
-
B.
Vincennes, Indiana
Vincennes, Indiana is a historic city in southwestern Indiana known as the state’s oldest city and an early frontier settlement along the Wabash River.
-
C.
Corydon
Corydon is a controversial series of dialogues by André Gide that defends homosexuality by arguing for its naturalness and historical prevalence.
-
D.
Lafayette, Indiana
Lafayette, Indiana is a mid-sized city in northwestern Indiana known as a regional economic and educational hub near Purdue University.
-
E.
Lafayette
Lafayette is a mid-sized city in southern Louisiana known as a cultural hub of Cajun and Creole music, food, and festivals.
- 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_69d8dcf94c288190a06dea029ae4b223 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5a6b620248190afa21a6ce61e2cff |
completed | April 20, 2026, 4:08 a.m. |
Created at: April 10, 2026, 11:55 a.m.