Triple
T9631835
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Langer |
E232824
|
entity |
| Predicate | hasFamilyName |
P18
|
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: [John Langer, hasFamilyName, Langer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Langer Context triple: [John Langer, hasFamilyName, 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.
Langhans
Langhans is a German surname most notably associated with Carl Gotthard Langhans, the architect of Berlin’s Brandenburg Gate.
-
D.
Lennertz
Lennertz is a German-origin surname borne by various individuals, including American composer Christopher Lennertz.
-
E.
Lerman
Lerman is a surname most notably associated with American actor Logan Lerman, known for roles in films such as the "Percy Jackson" series and "The Perks of Being a Wallflower."
- 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_69ca848940cc8190b97cec654cb3bb4a |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9b2621408190bfe2ea5a05359ee0 |
completed | April 1, 2026, 10:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d189fa706c819080e8ac2411f57d93 |
completed | April 4, 2026, 10 p.m. |
Created at: March 30, 2026, 8:11 p.m.