Triple
T7679059
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Erich von Falkenhayn |
E173941
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Erich |
E183749
|
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: Erich | Statement: [Erich von Falkenhayn, givenName, Erich]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Erich Context triple: [Erich von Falkenhayn, givenName, Erich]
-
A.
Erich
chosen
Erich is a masculine given name of German origin, commonly used in German-speaking countries and beyond.
-
B.
Helmut
Helmut is a masculine given name of German origin, historically common in German-speaking countries.
-
C.
Erich Roland
Erich Roland is a cinematographer known for his work on the documentary film "He Named Me Malala."
-
D.
Oskar
Oskar is a masculine given name of Germanic origin, commonly used in various European countries.
-
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_69c6995703e0819081de77361b602e78 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c701fe2cc88190b5fd5e1378c32e5b |
completed | March 27, 2026, 10:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8b4fa4f9c8190a1cd2c1aa173296d |
completed | March 29, 2026, 5:13 a.m. |
Created at: March 27, 2026, 4:01 p.m.