Triple
T8791116
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Iphigenia |
E209166
|
entity |
| Predicate | portrayedInWorkBy |
P32051
|
FINISHED |
| Object | Racine |
E492819
|
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: Racine | Statement: [Iphigenia, portrayedInWorkBy, Racine]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Racine Context triple: [Iphigenia, portrayedInWorkBy, Racine]
-
A.
Racine
Racine is a city in southeastern Wisconsin located on the shore of Lake Michigan, known historically for its manufacturing industry and Danish kringle pastries.
-
B.
Racine
Racine is a Chicago Transit Authority Blue Line rapid transit station serving the Near West Side of Chicago.
-
C.
Racine
chosen
Racine is a renowned 17th-century French dramatist celebrated for his classical tragedies such as "Phèdre" and "Andromaque."
-
D.
Stevens Point
Stevens Point is a small city in central Wisconsin known for its university, historic downtown, and access to outdoor recreation along the Wisconsin River.
-
E.
Kenosha
Kenosha is a mid-sized city in southeastern Wisconsin located on the shore of Lake Michigan between Milwaukee and Chicago.
- 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_69ca836168108190bb43d3dc235c1f55 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5f8e6e4881909155c40c52bc082c |
completed | March 31, 2026, 11:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf6f493f648190b0e9392f1abb44a1 |
completed | April 3, 2026, 7:42 a.m. |
Created at: March 30, 2026, 6:43 p.m.