Triple
T13339704
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Polk |
E317791
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Polk(e) |
E317791
|
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: Polk(e) | Statement: [Polk, hasVariant, Polk(e)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Polk(e) Context triple: [Polk, hasVariant, Polk(e)]
-
A.
Polk
chosen
Polk is a surname most prominently associated with James K. Polk, the 11th president of the United States.
-
B.
Polk
Polk is a Chicago Transit Authority 'L' station on the Pink Line serving the Near West Side near the Illinois Medical District.
-
C.
Perry
Perry is a fictional character played by British comedian and actress Katherine Lucy Bridget Burke, best known for her work in television comedy.
-
D.
Perry
Perry is one of the two socially awkward teenage protagonists in the British comedy film "Kevin & Perry Go Large," known for his obsession with clubbing, DJ culture, and losing his virginity.
-
E.
Perry
Perry is the central character in the film "Makers," around whom the story’s main events and themes revolve.
- 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_69d806b5a3c08190b42c267fb092f98a |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d99d01bf8481908cd3a99e5557b972 |
completed | April 11, 2026, 12:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f71f3ecf4c8190bb9eee699859dc08 |
completed | May 3, 2026, 10:11 a.m. |
Created at: April 9, 2026, 9:31 p.m.