Triple
T11388635
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Roger Henderson |
E269772
|
entity |
| Predicate | disrupts |
P7316
|
FINISHED |
| Object | Jessica Henderson's impending marriage |
—
|
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: Jessica Henderson's impending marriage | Statement: [Roger Henderson, disrupts, Jessica Henderson's impending marriage]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: disrupts Context triple: [Roger Henderson, disrupts, Jessica Henderson's impending marriage]
-
A.
disruptsContinuityOf
chosen
Indicates that one entity interrupts, breaks, or otherwise prevents the ongoing, uninterrupted progression or sequence of another entity or process.
-
B.
discourages
Indicates an action or influence that deters, dissuades, or reduces the likelihood of someone performing a particular behavior or pursuing a certain outcome.
-
C.
inhibits
Indicates that one entity prevents, restrains, or reduces the activity, effect, or occurrence of another entity.
-
D.
upset
Indicates that one entity causes another to feel distressed, unhappy, or emotionally disturbed.
-
E.
corrupts
Indicates that one entity causes another entity, system, or process to become morally, functionally, or structurally degraded or impaired.
- 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_69d6aacdbc6c8190af6dc3d5f5d22836 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d800160a1c81909d115bf89fe54a49 |
completed | April 9, 2026, 7:37 p.m. |
| PD | Predicate disambiguation | batch_69d7e70b228c8190b87f5101fd683788 |
completed | April 9, 2026, 5:51 p.m. |
Created at: April 8, 2026, 9:34 p.m.