Triple
T1905348
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bring Me a Unicorn: Diaries and Letters of Anne Morrow Lindbergh, 1922–1928 |
E37789
|
entity |
| Predicate | hasAuthorOccupationAsSubject |
P938
|
FINISHED |
| Object | writer |
—
|
LITERAL 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: writer | Statement: [Bring Me a Unicorn: Diaries and Letters of Anne Morrow Lindbergh, 1922–1928, hasAuthorOccupationAsSubject, writer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAuthorOccupationAsSubject Context triple: [Bring Me a Unicorn: Diaries and Letters of Anne Morrow Lindbergh, 1922–1928, hasAuthorOccupationAsSubject, writer]
-
A.
authorOccupation
chosen
Indicates the professional role or job that an author holds or is associated with.
-
B.
subjectOccupation
Indicates that the subject holds or performs a particular job, profession, or role as their occupation.
-
C.
notableWorkSubject
Indicates that a work is notably associated with a particular subject, such as a person, topic, or entity, as its primary focus or theme.
-
D.
creatorOccupation
Indicates the professional role or job that the creator of an entity holds or held.
-
E.
genreOfOccupation
Indicates the specific genre or category that characterizes a particular occupation or professional role.
- F. None of above.
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_69a8861be7148190a680937ec451a304 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb34d94fc8190a5bf1e582c77c725 |
completed | March 7, 2026, 5:10 a.m. |
| PD | Predicate disambiguation | batch_69abafe9f8b0819086d8f6288511c66d |
completed | March 7, 2026, 4:56 a.m. |
Created at: March 4, 2026, 7:35 p.m.