Triple
T14892684
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zuckerman novels |
E359789
|
entity |
| Predicate | characterTypeOfNathanZuckerman |
P60013
|
FINISHED |
| Object | novelist |
—
|
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: novelist | Statement: [Zuckerman novels, characterTypeOfNathanZuckerman, novelist]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterTypeOfNathanZuckerman Context triple: [Zuckerman novels, characterTypeOfNathanZuckerman, novelist]
-
A.
character1
Indicates that the subject is identified as the first or primary character in a narrative or context.
-
B.
typeOfCharacter
chosen
Indicates that one entity is a specific kind or category of character in relation to another entity.
-
C.
character2
Indicates that a second character entity is involved in the relationship or context defined by the predicate.
-
D.
character3
Indicates a tertiary or additional character role associated with an entity, typically the third distinct character linked within a given context or work.
-
E.
semiAutobiographicalCharacter
Indicates that a character is based partly on the real-life experiences, personality, or identity of its creator or author, but is not a fully direct self-portrayal.
- 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_69d827980cbc8190a0c569ae3940a1d9 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69ded5f9d10c819091732d7a5a42a682 |
completed | April 15, 2026, 12:04 a.m. |
| PD | Predicate disambiguation | batch_69de9a4a14a88190951bb8f4c60bd37b |
completed | April 14, 2026, 7:49 p.m. |
Created at: April 10, 2026, 2:10 a.m.