Triple
T3941927
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lange |
E92052
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Langer |
E44890
|
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: Langer | Statement: [Lange, hasVariant, Langer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Langer Context triple: [Lange, hasVariant, Langer]
-
A.
Langer
chosen
Langer is a surname most notably associated with Robert Langer, a pioneering American chemical engineer and prolific inventor in biotechnology and drug delivery.
-
B.
Lange
Lange is a German surname borne by numerous notable individuals across fields such as science, politics, arts, and sports.
-
C.
Relander
Relander is a Finnish surname most notably associated with Lauri Kristian Relander, the second President of Finland.
-
D.
Longo
Longo is an Italian surname borne by various notable figures in politics, arts, and sports.
-
E.
Lorens
Lorens is a character from Paulo Coelho’s novel "Brida," serving as one of the key figures in the protagonist’s spiritual and personal journey.
- 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_69aed965502c8190904ebad1203a4ae8 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeedfdffc48190a93ecb6115b2db33 |
completed | March 9, 2026, 3:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b533912bf881909e5e5e9e4245edbf |
completed | March 14, 2026, 10:08 a.m. |
Created at: March 9, 2026, 3:24 p.m.