Triple
T9352239
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kristen Schaal |
E225045
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Schaal |
E581977
|
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: Schaal | Statement: [Kristen Schaal, familyName, Schaal]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Schaal Context triple: [Kristen Schaal, familyName, Schaal]
-
A.
Schaal
chosen
Schaal is a surname most notably associated with American actress Wendy Schaal, known for her work in film and television voice acting.
-
B.
Schierke
Schierke is a small village in the Harz Mountains of Germany, known as a gateway to the Brocken peak and for its historic narrow-gauge railway connections and winter sports tourism.
-
C.
Gasselte
Gasselte is a village in the Dutch province of Drenthe, known for its surrounding forests, heathlands, and recreational lakes.
-
D.
Schaub
Schaub is a surname most prominently associated with former NFL quarterback Matt Schaub.
-
E.
Scheur
Scheur is a distributary branch of the Rhine–Meuse river system in the Netherlands that serves as an important shipping route in the Port of Rotterdam area.
- 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_69ca842abfd48190949d71c3b86eeba8 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd4f93a9848190ad2ae24f2aa607d2 |
completed | April 1, 2026, 5:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0e44f0a7881908b53a97715f4c6da |
completed | April 4, 2026, 10:13 a.m. |
Created at: March 30, 2026, 7:41 p.m.