Triple
T14724547
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peine |
E345903
|
entity |
| Predicate | hasSubdivision |
P747
|
FINISHED |
| Object | Telgte |
E990668
|
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: Telgte | Statement: [Peine, hasSubdivision, Telgte]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Telgte Context triple: [Peine, hasSubdivision, Telgte]
-
A.
Telgte
chosen
Telgte is a small historic town in western Germany, known for its religious pilgrimage traditions and picturesque old town along the Ems River.
-
B.
Tilgate
Tilgate is a residential neighbourhood and parkland area in the town of Crawley in West Sussex, England, known for Tilgate Park and its lakes and woodlands.
-
C.
Telnaes
Telnaes is the surname of Ann Telnaes, a Pulitzer Prize–winning editorial cartoonist known for her incisive political commentary.
-
D.
Tovere
Tovere is a small locality or hamlet that forms part of the municipality of Moltrasio in northern Italy’s Lake Como area.
-
E.
Landeskog
Landeskog is the surname of Swedish professional ice hockey player Gabriel Landeskog, a prominent NHL forward and longtime captain of the Colorado Avalanche.
- 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_69d822e5911c8190ba589f957dbd9ba7 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dec25e9a14819081fa06fc601f295d |
completed | April 14, 2026, 10:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fdf09791e081908a1262717fd31445 |
completed | May 8, 2026, 2:17 p.m. |
Created at: April 10, 2026, 1:29 a.m.