Triple
T13136815
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cal gymnastics |
E312104
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object | Cal |
E310291
|
NE 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: Cal | Statement: [Cal gymnastics, shortName, Cal]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cal Context triple: [Cal gymnastics, shortName, Cal]
-
A.
Cal
chosen
Cal is the commonly used short name for the University of California, Berkeley and its associated athletic programs.
-
B.
Cal
Cal is the given name of professional baseball pitcher Cal Quantrill.
-
C.
Cal
Cal is the nickname and given name of Cal McNair, the American businessman and principal owner of the NFL’s Houston Texans.
-
D.
Cal
Cal is the first name of Cal Henderson, a British software engineer best known as the co-founder and CTO of Slack.
-
E.
CAL
CAL is the commonly used abbreviation for the College of Arts and Letters, an academic division typically focused on humanities and liberal arts disciplines.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d806a9fe888190b081e2d9ea665d6c |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d981b53fbc8190a0f209c32a00f6cb |
completed | April 10, 2026, 11:03 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6e295b3408190a7246115d3ee90e5 |
completed | May 3, 2026, 5:52 a.m. |
Created at: April 9, 2026, 9:08 p.m.