Triple
T29239775
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fragments of the unfinished novel Answered Prayers |
E741288
|
entity |
| Predicate | effectOnAuthor |
P83240
|
FINISHED |
| Object | damaged Truman Capote’s social standing |
—
|
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: damaged Truman Capote’s social standing | Statement: [Fragments of the unfinished novel Answered Prayers, effectOnAuthor, damaged Truman Capote’s social standing]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: effectOnAuthor Context triple: [Fragments of the unfinished novel Answered Prayers, effectOnAuthor, damaged Truman Capote’s social standing]
-
A.
impactOnAuthor
chosen
Indicates that one entity has an effect, influence, or consequence on the author.
-
B.
effectOfPublication
Indicates the impact or consequence that a particular publication has on something, such as knowledge, behavior, policy, or subsequent events.
-
C.
impactOnPublisher
Indicates the effect or consequences that an action, event, or entity has on the publisher.
-
D.
effectOnUser
Indicates how an action, event, or condition influences or impacts a user.
-
E.
influencesFromAuthor
Indicates that one entity is affected or shaped by the actions, ideas, or characteristics of an author.
- 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_69f0911dd6fc819097d1abb287016489 |
completed | April 28, 2026, 10:51 a.m. |
| NER | Named-entity recognition | batch_69f70e8755a48190931eaa77946f9460 |
completed | May 3, 2026, 8:59 a.m. |
| PD | Predicate disambiguation | batch_69f70abc00848190a1c3f495ef6c8dc6 |
completed | May 3, 2026, 8:43 a.m. |
Created at: April 28, 2026, 12:30 p.m.