Triple
T9913866
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | De Dannan |
E185817
|
entity |
| Predicate | hasFormerMember |
P1168
|
FINISHED |
| Object | Mary Black |
E35689
|
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: Mary Black | Statement: [De Dannan, hasFormerMember, Mary Black]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mary Black Context triple: [De Dannan, hasFormerMember, Mary Black]
-
A.
Mary Black
chosen
Mary Black is an acclaimed Irish folk singer known for her influential solo career and interpretations of traditional and contemporary songs.
-
B.
Mary Durkan
Mary Durkan is an Irish politician known for her involvement in local and national public affairs.
-
C.
Mary Kathleen Williams
Mary Kathleen Williams is known primarily as the wife of American film and theater director Daniel Mann.
-
D.
Rosie Lyons
Rosie Lyons is a central character in the British dystopian drama series "Years and Years," representing an ordinary working-class woman navigating rapid social and political upheaval over several decades.
-
E.
Mavis Batey
Mavis Batey was a British codebreaker at Bletchley Park during World War II who played a key role in deciphering enemy communications, and later became a noted garden historian and author.
- 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_69ca829b45f481909040f7b99a1976ed |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cdb53ba1ac8190ba655133b81596d7 |
completed | April 2, 2026, 12:15 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d228ae315c8190bedb2cbab0982118 |
completed | April 5, 2026, 9:17 a.m. |
Created at: March 30, 2026, 8:41 p.m.