Triple
T23230946
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Luster |
E581151
|
entity |
| Predicate | borders |
P224
|
FINISHED |
| Object | Vang |
—
|
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: Vang | Statement: [Luster, borders, Vang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vang Context triple: [Luster, borders, Vang]
-
A.
Vang
chosen
Vang is a rural municipality in Innlandet county, Norway, known for its mountainous landscapes, traditional farming communities, and outdoor recreation opportunities.
-
B.
Vang
Vang is a Hmong family name shared by Bee Vang, an American actor known for his role in the film "Gran Torino."
-
C.
Vang valley
Vang valley is a scenic valley area in Vang municipality in Innlandet county, Norway, known for its lakes, mountains, and traditional rural landscapes.
-
D.
Le Vin
Le Vin is a section of Charles Baudelaire’s poetry collection Les Fleurs du mal that explores themes of intoxication, escape, and existential despair through the motif of wine.
-
E.
Winenne
Winenne is a small village in the municipality of Houyet in the Wallonia region of southern Belgium.
- 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_69e246043c48819089bae72c9a9c306c |
completed | April 17, 2026, 2:39 p.m. |
| NER | Named-entity recognition | batch_69f19231ef908190a791b4967916a66f |
completed | April 29, 2026, 5:08 a.m. |
Created at: April 17, 2026, 4:09 p.m.