Triple
T15372283
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lyness Royal Naval Cemetery |
E367579
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Lyness |
E75025
|
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: Lyness | Statement: [Lyness Royal Naval Cemetery, locatedNear, Lyness]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lyness Context triple: [Lyness Royal Naval Cemetery, locatedNear, Lyness]
-
A.
Lyness
chosen
Lyness is a small coastal village and former naval base on the island of Hoy in Orkney, Scotland.
-
B.
Loralai
Loralai is a town and district in northern Balochistan, Pakistan, known historically as a regional administrative and trade center.
-
C.
Norriss
Norriss is a variant spelling of the surname Norris, which is of English origin and historically associated with the meaning "northerner" or "from the north."
-
D.
Ainley
Ainley is an English surname most notably associated with actor Anthony Ainley, known for his role as the Master in the classic Doctor Who series.
-
E.
Lonna
Lonna is a small island near central Helsinki known for its historic military structures, public sauna, and seaside recreation.
- 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_69d85a1483788190ad93c2748e8af34b |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e5c1d548190930bfaf0861595ae |
completed | April 16, 2026, 1:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff0b528f408190b66d3d6e10e90a43 |
completed | May 9, 2026, 10:24 a.m. |
Created at: April 10, 2026, 3:18 a.m.