Triple
T17252615
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bietigheim-Bissingen |
E418793
|
entity |
| Predicate | locatedAtRiver |
P17819
|
FINISHED |
| Object | Metter |
E400065
|
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: Metter | Statement: [Bietigheim-Bissingen, locatedAtRiver, Metter]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Metter Context triple: [Bietigheim-Bissingen, locatedAtRiver, Metter]
-
A.
Metter
chosen
The Metter is a river in Germany that flows through the state of Baden-Württemberg and ultimately joins the Enz River.
-
B.
Mettet
Mettet is a municipality in Wallonia, Belgium, known for its rural character and the Circuit Jules Tacheny motor racing track.
-
C.
Metzad
Metzad is an Israeli settlement in the Gush Etzion region of the West Bank, known as a small religious community established after 1967.
-
D.
Mette
Mette is a given name most notably associated with American dancer and actress Mette Towley, known for her work in music videos and film.
-
E.
Marandellas
Marandellas is the former colonial-era name of Marondera, a town in eastern Zimbabwe known as an agricultural and educational center.
- 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_69d886d9ab108190b70edd8d17aa1204 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e42e6a1b648190a8bb2deb67bbdfdc |
completed | April 19, 2026, 1:22 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0170fb89248190ae431ce51dfeaffd |
completed | May 11, 2026, 6:02 a.m. |
Created at: April 10, 2026, 5:39 a.m.