Triple
T8002530
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Timothy Richard Ware |
E186284
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Ware |
E356752
|
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: Ware | Statement: [Timothy Richard Ware, familyName, Ware]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ware Context triple: [Timothy Richard Ware, familyName, Ware]
-
A.
Ware
Ware was the plaintiff in the landmark U.S. Supreme Court case Ware v. Hylton, which addressed the supremacy of federal treaties over conflicting state laws.
-
B.
Ware
chosen
Ware is a surname most prominently associated with DeMarcus Ware, a former NFL linebacker and defensive end known for his prolific pass-rushing career.
-
C.
Ware
Ware is a historic market town in Hertfordshire, England, known for its riverside setting on the River Lea and its long-standing role as a local commercial and coaching center.
-
D.
WD
WD is a consumer-facing brand of Western Digital known for its hard drives, solid-state drives, and other data storage products.
-
E.
WD
WD is the National Rail station code for Woodside railway station in London, England.
- 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_69ca82aaaf24819084b94d18f699ba53 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3cf2918081909ee0afab11caed63 |
completed | March 31, 2026, 3:18 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cbe121b0ac81908b9da58cf14c8df5 |
completed | March 31, 2026, 2:58 p.m. |
Created at: March 30, 2026, 5:18 p.m.