Triple
T15627833
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jean Smart |
E375728
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Smart |
E610310
|
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: Smart | Statement: [Jean Smart, familyName, Smart]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Smart Context triple: [Jean Smart, familyName, Smart]
-
A.
Smart
chosen
Smart is a surname most prominently associated in sports with Shaka Smart, a successful American college basketball coach.
-
B.
smart
smart is an automotive marque best known for its compact city cars and microcars, originally developed in partnership with Swatch and later owned by Mercedes-Benz.
-
C.
SMART
SMART is a commuter rail service operating in California’s Sonoma and Marin counties, providing passenger transportation along the North Bay corridor.
-
D.
SMART
SMART (South Metro Area Regional Transit) is a public transportation agency serving the southern Portland metropolitan area in Oregon with bus and transit services.
-
E.
Smart Tech
Smart Tech is the fictional electronics retail store where the main characters work in the comedy film "The 40-Year-Old Virgin."
- 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_69d85cd035a48190b73d5579ab73969a |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04eb4301881908c7157227fdf79b6 |
completed | April 16, 2026, 2:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff5f43191c81908c5704314a002608 |
completed | May 9, 2026, 4:22 p.m. |
Created at: April 10, 2026, 4:14 a.m.