Triple
T2512180
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wilhelm |
E52726
|
entity |
| Predicate | equivalentForm |
P6530
|
FINISHED |
| Object | Guillaume |
E16054
|
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: Guillaume | Statement: [Wilhelm, equivalentForm, Guillaume]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Guillaume Context triple: [Wilhelm, equivalentForm, Guillaume]
-
A.
Guillaume
chosen
Guillaume is the French form of the given name William, commonly used in French-speaking countries.
-
B.
Hugues
Hugues is a French given name, equivalent to the English name Hugh, historically borne by various nobles, clerics, and notable figures in French-speaking regions.
-
C.
Raoul
Raoul is a violent, masked intruder and one of the primary antagonists in the thriller film "Panic Room."
-
D.
Grégoire
Grégoire is the French form of the given name Gregory, commonly used in French-speaking countries.
-
E.
Geoffrey
Geoffrey is a masculine given name of English origin, famously borne by pioneering computer scientist and AI researcher Geoffrey Hinton.
- 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_69ab4958e76481908a235377dd921c9e |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abd1efb5c48190a9b47b39a388412b |
completed | March 7, 2026, 7:21 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af1faed61481909f13b8a014b39ed3 |
completed | March 9, 2026, 7:29 p.m. |
Created at: March 6, 2026, 9:46 p.m.