Triple
T15351482
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La Côte |
E367062
|
entity |
| Predicate | hasTown |
P847
|
FINISHED |
| Object | Gland |
E437776
|
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: Gland | Statement: [La Côte, hasTown, Gland]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gland Context triple: [La Côte, hasTown, Gland]
-
A.
Gland
chosen
Gland is a Swiss municipality in the canton of Vaud, situated on the shores of Lake Geneva between Geneva and Lausanne.
-
B.
Glandyfi
Glandyfi is a small hamlet in Ceredigion, Wales, known for its scenic setting near the River Dovey and the Cambrian Coast railway line.
-
C.
Grove
Grove is a large village and civil parish in Oxfordshire, England, situated near Wantage and known for its residential communities and local amenities.
-
D.
Grove
Grove is an unincorporated community in James City County, Virginia, known for its residential areas and proximity to historic Williamsburg and the James River.
-
E.
Grove
Grove is a surname most prominently associated with Andrew S. Grove, the influential engineer and former CEO of Intel who helped shape the modern semiconductor industry.
- 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_69d85a1355608190a6673ddb67231d54 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e290efc8190b22c95dcd3e5f57f |
completed | April 16, 2026, 1:40 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff01fd53688190939787a3d6ff3bb9 |
completed | May 9, 2026, 9:44 a.m. |
Created at: April 10, 2026, 3:17 a.m.