Triple
T10604057
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Douglas Brackman Jr. |
E275826
|
entity |
| Predicate | positionInFictionalFirm |
P87320
|
FINISHED |
| Object | managing partner |
—
|
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: managing partner | Statement: [Douglas Brackman Jr., positionInFictionalFirm, managing partner]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: positionInFictionalFirm Context triple: [Douglas Brackman Jr., positionInFictionalFirm, managing partner]
-
A.
positionInFiction
chosen
Indicates that one entity holds a specific role, status, or placement within a fictional work or narrative.
-
B.
setInFictionalOrganization
Indicates that an entity is located within, associated with, or takes place inside a fictional organization.
-
C.
positionInWork
Indicates the specific role, rank, or placement an entity holds within a larger work or structured composition.
-
D.
fictionalHeadquartersLocation
Indicates the place where a fictional organization, group, or entity is based or has its main headquarters within a fictional context.
-
E.
workLocationOfFictionalCharacter
Indicates the place or organization where a fictional character is depicted as working within their narrative context.
- 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_69d6aaf948d88190806cc3a8c47a3fb2 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d6df4992248190b640d743ccf02c82 |
completed | April 8, 2026, 11:05 p.m. |
| PD | Predicate disambiguation | batch_69d6dd72c1288190adbb5e79e94c044a |
completed | April 8, 2026, 10:57 p.m. |
Created at: April 8, 2026, 7:32 p.m.