Triple
T14147213
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ville Haute |
E350581
|
entity |
| Predicate | borders |
P224
|
FINISHED |
| Object | Grund |
E349450
|
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: Grund | Statement: [Ville Haute, borders, Grund]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Grund Context triple: [Ville Haute, borders, Grund]
-
A.
Grund
chosen
Grund is a historic, picturesque quarter of Luxembourg City known for its riverside setting, old architecture, and vibrant nightlife.
-
B.
Grossgrunden
Grossgrunden is one of the islands in the Holmön archipelago off the coast of northern Sweden in the Gulf of Bothnia.
-
C.
Grundy
Grundy is a surname of English origin borne by various notable individuals across politics, sports, and entertainment.
-
D.
GROND
GROND is a multi-channel optical and near-infrared imaging instrument designed primarily for rapid follow-up observations of gamma-ray bursts and other transient astronomical events.
-
E.
Gruden
Gruden is a surname most prominently associated with Jon Gruden, a former NFL head coach and television analyst.
- 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_69d827865f608190b311820428ae027b |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de612266248190a8591b646fe30ae6 |
completed | April 14, 2026, 3:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fcdf1d8c448190bd223258b28fecc9 |
completed | May 7, 2026, 6:51 p.m. |
Created at: April 10, 2026, 12:54 a.m.