Triple
T21055381
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anne More |
E518694
|
entity |
| Predicate | effectOfMarriage |
P142664
|
FINISHED |
| Object | damage to John Donne’s career |
—
|
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: damage to John Donne’s career | Statement: [Anne More, effectOfMarriage, damage to John Donne’s career]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: effectOfMarriage Context triple: [Anne More, effectOfMarriage, damage to John Donne’s career]
-
A.
effectOnMaritalRelations
Indicates how an action, event, or condition influences the quality, stability, or dynamics of marital relationships between partners.
-
B.
marriageOutcome
Indicates the result or status that follows from a marriage, such as whether it continues, ends, or changes form.
-
C.
divorceEffect
Indicates the legal and relational consequences that result from a divorce between two parties.
-
D.
marriageContext
Indicates the situational or cultural circumstances under which a marriage occurs or exists, such as legal, social, or religious conditions surrounding the marital relationship.
-
E.
maritalPolicy
Indicates a relationship where an authority or institution defines rules, conditions, or norms governing marriage between individuals.
- 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_69e0b5053ac48190921529544959e906 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e6fd7edb8481908e4dc7573f7fa98f |
completed | April 21, 2026, 4:30 a.m. |
| PD | Predicate disambiguation | batch_69e5dbf9d71881908cd85dfc37db93ca |
completed | April 20, 2026, 7:55 a.m. |
| PDg | Predicate description generation | batch_69e5e2e03d88819086f8b641656ad8b0 |
completed | April 20, 2026, 8:25 a.m. |
Created at: April 16, 2026, 2:36 p.m.