Triple
T17784787
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grabs |
E443988
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object | Studen |
—
|
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: Studen | Statement: [Grabs, hasSettlement, Studen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Studen Context triple: [Grabs, hasSettlement, Studen]
-
A.
Studen
chosen
Studen is a municipality in the canton of Bern in Switzerland, known for its residential character and proximity to the city of Biel/Bienne.
-
B.
Estudiantes
Estudiantes is a historic Spanish basketball club from Madrid, known for its strong youth academy and long-standing presence in the top tiers of Spanish basketball.
-
C.
Estudiantes
Estudiantes is the nickname of Tecos F.C., a Mexican football club historically associated with the Universidad Autónoma de Guadalajara.
-
D.
Estudiantes
Estudiantes is a historic Argentine football club from La Plata known for its passionate fan base and multiple domestic and international titles.
-
E.
The Student
The Student is a fictional character in Henry Wadsworth Longfellow’s narrative poem collection "Tales of a Wayside Inn," representing one of the storytellers gathered at the inn.
- 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_69d8b9ef17708190bdf7e2adbf14ddc2 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e48791dec0819090d6e88449389fc0 |
completed | April 19, 2026, 7:43 a.m. |
Created at: April 10, 2026, 10:12 a.m.