Triple
T15095599
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mary Tyrone |
E360527
|
entity |
| Predicate | occupationBeforeMarriage |
P105342
|
FINISHED |
| Object | convent school student |
—
|
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: convent school student | Statement: [Mary Tyrone, occupationBeforeMarriage, convent school student]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: occupationBeforeMarriage Context triple: [Mary Tyrone, occupationBeforeMarriage, convent school student]
-
A.
preMarriageOccupation
chosen
Indicates the occupation or job role a person held before getting married.
-
B.
partnerBeforeMarriage
Indicates that one entity was the romantic or life partner of another entity prior to their marriage.
-
C.
marriageBefore
Indicates that one marriage event occurred earlier in time than another marriage event.
-
D.
marriedBefore
Indicates that one entity entered into a marriage at an earlier time than the other entity.
-
E.
marriedToFormerOccupation
Indicates that a person is married to someone who previously held a specified occupation.
- 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_69d85a035aa88190b52a139d3a1b7b6d |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e005466e9c8190a68e1fbeb8922b1a |
completed | April 15, 2026, 9:38 p.m. |
| PD | Predicate disambiguation | batch_69deb9645b9c8190a5712456dbd78029 |
completed | April 14, 2026, 10:02 p.m. |
Created at: April 10, 2026, 3:04 a.m.