Triple
T36028390
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tom Spanbauer |
E1042192
|
entity |
| Predicate | hasWritingWorkshop |
P184497
|
FINISHED |
| Object | Dangerous Writing |
—
|
NE NERFINISHED |
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: Dangerous Writing | Statement: [Tom Spanbauer, hasWritingWorkshop, Dangerous Writing]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWritingWorkshop Context triple: [Tom Spanbauer, hasWritingWorkshop, Dangerous Writing]
-
A.
hasWrittenWorkType
Indicates that an entity (typically a written work) is associated with a specific type or category of written work (such as novel, article, report, etc.).
-
B.
hasWrittenFor
Indicates that one entity has created written content (such as articles, stories, or texts) for or on behalf of another entity, typically a publication, organization, or platform.
-
C.
usesWriting
Indicates that one entity employs or applies a particular writing system, script, or written form for communication or representation.
-
D.
usesWritingCenter
Indicates that an entity makes use of the services or resources provided by a writing center.
-
E.
willWrittenIn
Indicates that a legal will was authored, drafted, or formally written in a specified location or jurisdiction.
- F. None of above. chosen
Provenance (4 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_69f76e2c568881909e1e21f85252b0f0 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7b35e32d481909ef0220e6f6ff4a8 |
completed | May 3, 2026, 8:43 p.m. |
| PD | Predicate disambiguation | batch_69f7b1bad2e88190963ab4ee5d4f2038 |
completed | May 3, 2026, 8:36 p.m. |
| PDg | Predicate description generation | batch_69f7b2c66054819083897e25edb65ba7 |
completed | May 3, 2026, 8:40 p.m. |
Created at: May 3, 2026, 4:07 p.m.