Triple
T3033716
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lillehammer railway station |
E82956
|
entity |
| Predicate | connectsTo |
P845
|
FINISHED |
| Object | Hamar |
E68670
|
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: Hamar | Statement: [Lillehammer railway station, connectsTo, Hamar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hamar Context triple: [Lillehammer railway station, connectsTo, Hamar]
-
A.
Hamar
chosen
Hamar is a town and municipality in Innlandet county, Norway, known for its rich Viking history and as a regional cultural and administrative center.
-
B.
Sassoun
Sassoun is a mountainous region in historic Western Armenia, famed in Armenian folklore as the homeland of the legendary heroes of the national epic.
-
C.
Hatti
Hatti was an ancient Anatolian kingdom and cultural region centered in central Turkey, later absorbed into the Hittite Empire.
-
D.
Mora
Mora is a surname of Hungarian origin most notably borne by the German-Hungarian writer Terézia Mora.
-
E.
Behdet
Behdet is an ancient Egyptian cult center in the Nile Delta particularly associated with the worship of the god Horus.
- 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_69ad8b21a62881908ec5dd4fba4a187c |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9af13ce48190bda4f5ca0ffe6285 |
completed | March 8, 2026, 3:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b1eeeeff988190bb664c75d54d93d8 |
completed | March 11, 2026, 10:38 p.m. |
Created at: March 8, 2026, 3:01 p.m.