Triple
T27409119
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Deanna Dwyer |
E692091
|
entity |
| Predicate | realNameOccupation |
P12884
|
FINISHED |
| Object | bestselling author |
—
|
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: bestselling author | Statement: [Deanna Dwyer, realNameOccupation, bestselling author]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: realNameOccupation Context triple: [Deanna Dwyer, realNameOccupation, bestselling author]
-
A.
namedPersonOccupation
chosen
Indicates that a person is explicitly identified as having a particular occupation or job role.
-
B.
namesakeOccupation
Indicates that one entity’s occupation is the same as, or derived from, the occupation associated with the other entity’s namesake.
-
C.
realName
Indicates that one entity is the actual, full, or birth name of another entity, which may be known by an alias, nickname, or alternate identity.
-
D.
characterFormerOccupation
Indicates that a character previously held a specific occupation but no longer does.
-
E.
sonOccupation
Indicates that a specified occupation is the job or professional role held by a person's son.
- 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_69ef5205fc808190ad3efc5525b8e6d6 |
completed | April 27, 2026, 12:09 p.m. |
| NER | Named-entity recognition | batch_69f62cd99324819099a95e2729e966e9 |
completed | May 2, 2026, 4:56 p.m. |
| PD | Predicate disambiguation | batch_69f623aaf40081909f947431424a1d55 |
completed | May 2, 2026, 4:17 p.m. |
Created at: April 27, 2026, 12:31 p.m.