Triple
T295583
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | William Tudor |
E6085
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | William |
E772
|
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: William | Statement: [William Tudor, givenName, William]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: William Context triple: [William Tudor, givenName, William]
-
A.
William
chosen
William is a common masculine given name of Germanic origin, widely used in English-speaking countries.
-
B.
Thomas
Thomas is the given name of Thomas Malthus, the influential English economist and demographer known for his theories on population growth and resource limits.
-
C.
Thomas
Thomas is the full given name of Tom Brady, the legendary NFL quarterback widely regarded as one of the greatest players in American football history.
-
D.
John
John was a medieval English monarch who ruled as King John of England from 1199 to 1216 and is best known for sealing the Magna Carta.
-
E.
John
John is the given name of John Bardeen, the American physicist who uniquely won the Nobel Prize in Physics twice for his work on the transistor and superconductivity.
- 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_69a2e79114b081909490b3bf5a5dbb51 |
completed | Feb. 28, 2026, 1:03 p.m. |
| NER | Named-entity recognition | batch_69a2e979663481908cf9622e59fed041 |
completed | Feb. 28, 2026, 1:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a3fe8dde4c8190bd897e5251eb6941 |
completed | March 1, 2026, 8:53 a.m. |
Created at: Feb. 28, 2026, 1:06 p.m.