Triple
T8802182
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michael Wisdom |
E209436
|
entity |
| Predicate | hasSurname |
P18
|
FINISHED |
| Object | Wisdom |
E39718
|
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: Wisdom | Statement: [Michael Wisdom, hasSurname, Wisdom]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wisdom Context triple: [Michael Wisdom, hasSurname, Wisdom]
-
A.
Wisdom
Wisdom is a deuterocanonical book of the Old Testament, traditionally attributed to Solomon, that reflects on divine wisdom, justice, and the destiny of the righteous.
-
B.
Wisdom
chosen
Wisdom is a common English surname borne by various notable individuals, including the British comedian and actor Norman Wisdom.
-
C.
Wise
Wise is a London-based financial technology company best known for its low-cost international money transfer and multi-currency account services.
-
D.
Wise
Wise is a surname shared by various notable individuals across fields such as entertainment, politics, and academia.
-
E.
Sagesse
Sagesse is a collection of deeply spiritual and introspective poems by Paul Verlaine, reflecting his religious crisis and search for inner peace.
- 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_69ca836320e48190b5cf585b90a322c4 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5fb9c5c88190881b069e1face10c |
completed | March 31, 2026, 11:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf6f799f00819089159da177c816e9 |
completed | April 3, 2026, 7:42 a.m. |
Created at: March 30, 2026, 6:44 p.m.