Triple
T6787037
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kove language |
E155833
|
entity |
| Predicate | hasAlternateName |
P39
|
FINISHED |
| Object | Kove |
E619351
|
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: Kove | Statement: [Kove language, hasAlternateName, Kove]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kove Context triple: [Kove language, hasAlternateName, Kove]
-
A.
Kove
chosen
Kove is an Austronesian language spoken in coastal communities of New Britain in Papua New Guinea.
-
B.
Kopervik
Kopervik is a coastal town in Rogaland county, Norway, situated on the island of Karmøy and serving as an important local commercial and administrative center.
-
C.
Kostava
Kostava is a Georgian surname most notably borne by Merab Kostava, a prominent Soviet-era Georgian dissident and national independence activist.
-
D.
Kamen
Kamen is a town in North Rhine-Westphalia, Germany, known as a local industrial and transport hub in the Ruhr region.
-
E.
Kamen
Kamen is a surname most prominently associated with American inventor and entrepreneur Dean Kamen, known for creating the Segway and numerous medical devices.
- 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_69c6881770fc8190972b2906390380f5 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d2907d0081908291aad66048b8b1 |
completed | March 27, 2026, 6:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c723cc35cc8190b5affdfd363171ba |
completed | March 28, 2026, 12:41 a.m. |
Created at: March 27, 2026, 2:14 p.m.