Triple
T18139367
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | PCC |
E434219
|
entity |
| Predicate | hasComponent |
P35
|
FINISHED |
| Object | BIBCO |
—
|
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: BIBCO | Statement: [PCC, hasComponent, BIBCO]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: BIBCO Context triple: [PCC, hasComponent, BIBCO]
-
A.
BIBCO
chosen
BIBCO is a cooperative cataloging program through which participating libraries create and share high-quality bibliographic records to support standardized access to library collections.
-
B.
Blodgett
Blodgett is a surname of English origin borne by various notable individuals across fields such as politics, business, and the arts.
-
C.
Brinker
Brinker is the surname of Nancy Goodman Brinker, the American businesswoman and philanthropist who founded the Susan G. Komen breast cancer organization.
-
D.
Belasco
Belasco is a surname most notably associated with David Belasco, a prominent American theatrical producer, director, and playwright of the late 19th and early 20th centuries.
-
E.
Fischer’s
Fischer’s is a renowned London restaurant known for its classic Viennese café style, serving Central European dishes in an elegant, old-world setting.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b90aac308190801e2c57d8c5bfe5 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4de0993e88190b19c5cb35a6d252d |
completed | April 19, 2026, 1:52 p.m. |
Created at: April 10, 2026, 10:29 a.m.