Triple
T12510520
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Denise Lombardo |
E299063
|
entity |
| Predicate | preMarriageOccupation |
P105342
|
FINISHED |
| Object | sales employee |
—
|
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: sales employee | Statement: [Denise Lombardo, preMarriageOccupation, sales employee]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: preMarriageOccupation Context triple: [Denise Lombardo, preMarriageOccupation, sales employee]
-
A.
partnerBeforeMarriage
Indicates that one entity was the romantic or life partner of another entity prior to their marriage.
-
B.
residenceBeforeMarriage
Indicates that an individual lived at a particular residence prior to their marriage.
-
C.
spouseOccupation
Indicates that one person’s spouse has a particular job, profession, or occupation.
-
D.
earlierOccupation
Indicates that one occupation held by an entity occurred before another occupation in that entity’s work history.
-
E.
roleInSpouseCareer
Indicates the nature or extent of a person’s involvement or influence in their spouse’s professional career.
- 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_69d6ada4cd388190ae3bbf83ff87057a |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d954b867dc8190af8a70f797e4d133 |
completed | April 10, 2026, 7:51 p.m. |
| PD | Predicate disambiguation | batch_69d954096af88190b6be81b008c82139 |
completed | April 10, 2026, 7:48 p.m. |
| PDg | Predicate description generation | batch_69d954b715fc819091fa84430be46273 |
completed | April 10, 2026, 7:51 p.m. |
Created at: April 8, 2026, 9:57 p.m.