Triple
T16891138
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bob Rumson |
E424173
|
entity |
| Predicate | fictionalOfficeHeld |
P75581
|
FINISHED |
| Object | U.S. senator |
—
|
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: U.S. senator | Statement: [Bob Rumson, fictionalOfficeHeld, U.S. senator]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fictionalOfficeHeld Context triple: [Bob Rumson, fictionalOfficeHeld, U.S. senator]
-
A.
possibleOfficeHeld
Indicates that an entity may have held, or is a candidate to have held, a particular office or position, without asserting it as a confirmed fact.
-
B.
fictionalOfficeContested
Indicates that a fictional political or organizational office is being sought or competed for by one or more characters within a narrative.
-
C.
officeHolderRoleIn
chosen
Indicates that an entity holds or has held a specific role or position within a particular office or organizational context.
-
D.
typeOfOffice
Indicates the specific category or kind of office that an office entity belongs to (e.g., executive, legislative, judicial, or other office types).
-
E.
officeHolderRoleFor
Indicates that a specific role or position is held by an office holder within an organization or governing body.
- 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_69d889da3e8c8190a2b118f383f0beac |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e3bbc473d4819090cfea374ef5ca49 |
completed | April 18, 2026, 5:13 p.m. |
| PD | Predicate disambiguation | batch_69e32b90ec3c819099c51bb7baf2984c |
completed | April 18, 2026, 6:58 a.m. |
Created at: April 10, 2026, 5:29 a.m.