Triple
T164618
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Isle of Man |
E2985
|
entity |
| Predicate | largestTown |
P235
|
FINISHED |
| Object | Douglas |
E80
|
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: Douglas | Statement: [Isle of Man, largestTown, Douglas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Douglas Context triple: [Isle of Man, largestTown, Douglas]
-
A.
Douglas
chosen
Douglas is a masculine given name of Scottish origin that has been widely used in English-speaking countries.
-
B.
Montrose
Montrose is a coastal town in eastern Scotland known for its historic harbor, sandy beach, and surrounding nature reserves.
-
C.
Everett
Everett is a surname of English origin borne by various notable individuals, including American politician and orator Edward Everett.
-
D.
Marshall
Marshall is a common English surname borne by numerous notable figures, including military leaders, politicians, and artists.
-
E.
Richardson
Richardson is a suburban city in the Dallas–Fort Worth metropolitan area known for its telecommunications industry and the University of Texas at Dallas.
- 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_69a2524ce1e48190ab066bf72859f474 |
completed | Feb. 28, 2026, 2:26 a.m. |
| NER | Named-entity recognition | batch_69a258827da481909b20ea5e9d21676f |
completed | Feb. 28, 2026, 2:52 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a36ceb0244819092fdbbd095804b83 |
completed | Feb. 28, 2026, 10:32 p.m. |
Created at: Feb. 28, 2026, 2:34 a.m.