Triple
T30932815
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Scranton branch |
E788040
|
entity |
| Predicate | hasReceptionistCharacter |
P2972
|
FINISHED |
| Object | Pam Beesly |
—
|
NE NERFINISHED |
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: Pam Beesly | Statement: [Scranton branch, hasReceptionistCharacter, Pam Beesly]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasReceptionistCharacter Context triple: [Scranton branch, hasReceptionistCharacter, Pam Beesly]
-
A.
roleInReception
Indicates the specific function or capacity an entity serves within the context of a reception event.
-
B.
hasFrontDesk
Indicates that one entity provides or is equipped with a front desk service or reception area for another entity.
-
C.
hasReception
Indicates that an entity hosts, includes, or is associated with a reception event (such as a formal gathering or welcoming function).
-
D.
hasOnlineReception
Indicates that an entity provides or supports a reception, front-desk, or greeting function through online or digital channels rather than solely in person.
-
E.
hasClerk
chosen
Indicates that an entity is served, assisted, or managed by a clerk associated with it.
- 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_69f224c0b7fc819090cb89df60d23653 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_6a00bb3a6f888190b3ecd0fbc9af9b4a |
completed | May 10, 2026, 5:07 p.m. |
| PD | Predicate disambiguation | batch_6a00b902dbf881909e098ff102b7ea7e |
completed | May 10, 2026, 4:57 p.m. |
Created at: April 29, 2026, 8:52 p.m.