Triple
T7500600
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Emanuel Lasker |
E177249
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Emanuel |
E147057
|
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: Emanuel | Statement: [Emanuel Lasker, givenName, Emanuel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Emanuel Context triple: [Emanuel Lasker, givenName, Emanuel]
-
A.
Emanuel
Emanuel is a surname most prominently associated with Rahm Emanuel, the American politician and former mayor of Chicago.
-
B.
Emmanuel
Emmanuel is a Belgian prince, a member of the royal family of Belgium and the son of King Philippe and Queen Mathilde.
-
C.
Emmanuel
chosen
Emmanuel is a masculine given name of Hebrew origin meaning "God is with us," used in various languages and cultures.
-
D.
Emanuele
Emanuele is an Italian given name, equivalent to Emmanuel, commonly used for males in Italian-speaking regions.
-
E.
Erwin
Erwin is a masculine given name of German origin, historically associated with figures such as the World War II field marshal Erwin Rommel.
- 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_69c69f2696688190915a8458f2398211 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f59aabb8819085bdbe9c793d5b8b |
completed | March 27, 2026, 9:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c83c9490fc81908d35c0537b45aa13 |
completed | March 28, 2026, 8:39 p.m. |
Created at: March 27, 2026, 3:44 p.m.