Triple
T31651860
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pyncheon family |
E807749
|
entity |
| Predicate | originOfCurse |
P203003
|
FINISHED |
| Object | execution of Matthew Maule (in the novel) |
—
|
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: execution of Matthew Maule (in the novel) | Statement: [Pyncheon family, originOfCurse, execution of Matthew Maule (in the novel)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: originOfCurse Context triple: [Pyncheon family, originOfCurse, execution of Matthew Maule (in the novel)]
-
A.
typeOfCurse
Indicates that one entity is a specific kind or category of curse in relation to another entity.
-
B.
placeOfCurse
Indicates the specific location where a curse is cast, placed, or takes effect on an entity.
-
C.
associatedCurse
Indicates that one entity is linked to, affected by, or bears responsibility for a particular curse related to another entity.
-
D.
curseName
Indicates that one entity assigns, uses, or is associated with a specific name or label used as a curse toward another entity.
-
E.
explainsCurseTo
Indicates that one entity provides information or clarification about a curse to another entity.
- 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_69f348daf95c81908b4c985b7ddcd0b3 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_6a00d9717a9881908194163d719f14d6 |
completed | May 10, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a00d91db2ec81909ebacfc9f0d11dd8 |
completed | May 10, 2026, 7:14 p.m. |
| PDg | Predicate description generation | batch_6a00d970a11081908f24876a0696d827 |
completed | May 10, 2026, 7:16 p.m. |
Created at: April 30, 2026, 10:53 p.m.