Triple
T14619080
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Darren |
E343168
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Darien |
E684061
|
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: Darien | Statement: [Darren, hasVariant, Darien]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Darien Context triple: [Darren, hasVariant, Darien]
-
A.
Darien
chosen
Darien is a coastal town in Fairfield County, Connecticut, known for its affluent residential character and location along Long Island Sound.
-
B.
Demer
Demer is a river in Belgium that flows through the provinces of Limburg and Flemish Brabant before joining the Dijle.
-
C.
Wando
Wando was a Canadian Thoroughbred racehorse best known for winning the 2003 Canadian Triple Crown and later standing at stud.
-
D.
Wando
Wando is a coastal city and island hub in South Jeolla Province, South Korea, known for its fisheries, seaweed production, and scenic maritime landscapes.
-
E.
Bayaguana
Bayaguana is a historic town and municipality in the Monte Plata province of the Dominican Republic, known for its religious traditions and rural agricultural character.
- 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_69d822dffc3c8190aa173b90761bffda |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb46550e48190af45f426f02579bb |
completed | April 14, 2026, 9:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fdd5cd890c8190902c9b526babe6d3 |
completed | May 8, 2026, 12:23 p.m. |
Created at: April 10, 2026, 1:25 a.m.