Triple
T19734522
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sharon Valerii |
E473941
|
entity |
| Predicate | homeWorld |
P4624
|
FINISHED |
| Object | Troy (implied backstory) |
—
|
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: Troy (implied backstory) | Statement: [Sharon Valerii, homeWorld, Troy (implied backstory)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Troy (implied backstory) Context triple: [Sharon Valerii, homeWorld, Troy (implied backstory)]
-
A.
Troy
Troy is a historic city in eastern New York State, known for its 19th-century architecture and role in the Industrial Revolution as a major manufacturing center.
-
B.
Troy
Troy is the legendary ancient city in Asia Minor that was the focal point of the Trojan War in Greek and Roman mythology.
-
C.
Troy
Troy is a masculine given name of ancient origin, famously borne by former NFL quarterback Troy Aikman.
-
D.
Troy
Troy was a top-class British Thoroughbred racehorse best known for his dominant 1979 Epsom Derby victory and status as one of the outstanding middle-distance performers of his era.
-
E.
Troy
Troy is a small city in southeastern Alabama known for being the home of Troy University and its vibrant college-town atmosphere.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide. chosen
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_69d8e517ebd48190979ee76723bcfadf |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6515d138c8190a4c4d112ed5756a3 |
completed | April 20, 2026, 4:16 p.m. |
Created at: April 10, 2026, 1:47 p.m.