Triple
T5911529
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Q’s |
E131468
|
entity |
| Predicate | homeState |
P562
|
FINISHED |
| Object | California |
E26
|
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: California | Statement: [Q’s, homeState, California]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: California Context triple: [Q’s, homeState, California]
-
A.
Kalifornia
Kalifornia is a 1993 neo-noir road thriller film that follows a journalist couple researching serial killers while unknowingly traveling with one.
-
B.
California, United States
chosen
California, United States is a large and populous U.S. state on the West Coast known for its diverse geography, major technology and entertainment industries, and cultural and economic influence.
-
C.
CA
CA is the two-letter ISO 3166-1 alpha-2 country code that uniquely identifies Canada in international standards and systems.
-
D.
CA
CA is the vehicle registration code used on license plates for the Italian city of Cagliari.
-
E.
CA
CA is the commonly used abbreviation for Club Africain, a major Tunisian multi-sport club best known for its football team based in Tunis.
- 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_69c008593a44819081a07ae0efe6c574 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c037b6f48c8190b60a32e5f64fe254 |
completed | March 22, 2026, 6:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0c015b90081909777ac5ed80e927c |
completed | March 23, 2026, 4:22 a.m. |
Created at: March 22, 2026, 3:59 p.m.