Triple
T15508702
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Norman MacLeod (minister, born 1812) |
E368648
|
entity |
| Predicate | editorOf |
P1954
|
FINISHED |
| Object | Good Words |
E1160506
|
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: Good Words | Statement: [Norman MacLeod (minister, born 1812), editorOf, Good Words]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Good Words Context triple: [Norman MacLeod (minister, born 1812), editorOf, Good Words]
-
A.
Good Words
chosen
Good Words was a popular 19th-century British religious and literary magazine that combined Christian instruction with general interest articles and fiction.
-
B.
The Words
The Words is Jean-Paul Sartre’s autobiographical work in which he reflects on his childhood and the development of his literary and philosophical identity.
-
C.
The Words
The Words is a 2012 drama film about a struggling writer who achieves fame by passing off another man's manuscript as his own, exploring themes of authorship, guilt, and moral consequence.
-
D.
Big Words
Big Words is a highly intelligent and technically skilled member of DC Comics' Newsboy Legion, often serving as the group's resident inventor and problem-solver.
-
E.
Of Words
"Of Words" is a chapter in Book III that examines the nature, use, and significance of language and terminology.
- 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_69d85a1794cc8190b0b428716296e63e |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03fd008708190a3657863eb9ac626 |
completed | April 16, 2026, 1:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff3d4cf35c8190aa8d2db6dd744c3f |
completed | May 9, 2026, 1:57 p.m. |
Created at: April 10, 2026, 3:55 a.m.