Triple
T6076070
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Martinsried campus |
E135402
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Martinsried |
E534329
|
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: Martinsried | Statement: [Martinsried campus, locatedIn, Martinsried]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Martinsried Context triple: [Martinsried campus, locatedIn, Martinsried]
-
A.
Martinsried
chosen
Martinsried is a village near Munich, Germany, known as a major hub for life sciences and biotechnology research.
-
B.
Fürstenfeldbruck
Fürstenfeldbruck is a town in Upper Bavaria, Germany, known for its historic monastery, proximity to Munich, and nearby air base.
-
C.
Garching
Garching is a Bavarian town near Munich known as a major science and research hub, hosting numerous institutes and facilities including a large campus of the Technical University of Munich.
-
D.
Erding
Erding is a Bavarian town northeast of Munich, best known for its historic center, Erdinger Weißbräu brewery, and large thermal spa complex.
-
E.
Adlershof
Adlershof is a district in Berlin, Germany, known as a major science, technology, and media hub featuring research institutes, universities, and high-tech companies.
- 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_69c0087ad31c8190ab936e0ff28614b6 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c0575ec63081908a868a41855acf73 |
completed | March 22, 2026, 8:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c11d43f7908190845c2337cd243a3c |
completed | March 23, 2026, 11 a.m. |
Created at: March 22, 2026, 4:11 p.m.