Triple
T4286727
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Taunus |
E97286
|
entity |
| Predicate | containsTown |
P847
|
FINISHED |
| Object | Eppstein |
E209320
|
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: Eppstein | Statement: [Taunus, containsTown, Eppstein]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Eppstein Context triple: [Taunus, containsTown, Eppstein]
-
A.
Eppstein
chosen
Eppstein is a small historic town in the German state of Hesse, known for its medieval castle and scenic location in the Taunus mountains.
-
B.
Charikar
Charikar is a city in northern Afghanistan that serves as the capital of Parwan Province and a key hub on the route between Kabul and the northern regions.
-
C.
Bryc
Bryc is an alternative spelling of the given name Bryce, typically used as a modern or stylistic variant.
-
D.
Eisenberg
Eisenberg is a surname most notably associated with American actress Hallie Kate Eisenberg.
-
E.
S. Rao Kosaraju
S. Rao Kosaraju is a computer scientist known for his contributions to algorithm design and graph theory, including early work on strongly connected components algorithms.
- 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_69b3454595848190a0e6bbb6a2bea040 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3505d23d88190a638f2cc2acee9ee |
completed | March 12, 2026, 11:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5b7c8c1ac819086ded38c1abe9b63 |
completed | March 14, 2026, 7:32 p.m. |
Created at: March 12, 2026, 11:08 p.m.