Triple
T201095
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chris Cornell |
E4505
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Christopher |
E16453
|
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: Christopher | Statement: [Chris Cornell, givenName, Christopher]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Christopher Context triple: [Chris Cornell, givenName, Christopher]
-
A.
Christopher
chosen
Christopher is the full given name of Chris Sununu, an American politician who has served as governor of New Hampshire.
-
B.
Robert
Robert is a common masculine given name of Germanic origin, widely used in English-speaking countries.
-
C.
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.
-
D.
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.
-
E.
Anthony
Anthony is a masculine given name of Latin origin, commonly used in English-speaking countries.
- 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_69a25737567c81908f9c505300239181 |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25be47ea881909c296b30a0d47a65 |
completed | Feb. 28, 2026, 3:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a3fe8c0d4881909164199221272daa |
completed | March 1, 2026, 8:53 a.m. |
Created at: Feb. 28, 2026, 2:51 a.m.