Triple
T25479840
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frank Pallone Jr. |
E638537
|
entity |
| Predicate | has experience in |
P136773
|
FINISHED |
| Object | state-level legislation |
—
|
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: state-level legislation | Statement: [Frank Pallone Jr., has experience in, state-level legislation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: has experience in Context triple: [Frank Pallone Jr., has experience in, state-level legislation]
-
A.
hasExperienceOf
Indicates that one entity has undergone, encountered, or lived through a particular event, situation, or activity associated with another entity.
-
B.
hasPastExperience
chosen
Indicates that an entity has previously engaged in or undergone the specified activity, role, or situation in the past.
-
C.
usedExperienceIn
Indicates that an entity applied or leveraged a particular experience or expertise in performing an action or achieving a result.
-
D.
experienceIncludes
Indicates that a particular experience encompasses, contains, or involves a specified component, activity, or element as part of it.
-
E.
typeOfExperience
Indicates that one entity specifies the category or nature of an experience associated with another entity.
- 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_69e75dbabeac8190bab30628f8b799d4 |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f5f775776881909086b4249307c522 |
completed | May 2, 2026, 1:09 p.m. |
| PD | Predicate disambiguation | batch_69f468421ba08190880eac99135e5970 |
completed | May 1, 2026, 8:45 a.m. |
Created at: April 21, 2026, 2:30 p.m.