Triple
T13149909
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | German Templer Society |
E312436
|
entity |
| Predicate | foundedColony |
P160
|
FINISHED |
| Object | Wilhelma |
E193563
|
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: Wilhelma | Statement: [German Templer Society, foundedColony, Wilhelma]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wilhelma Context triple: [German Templer Society, foundedColony, Wilhelma]
-
A.
Wilhelma
chosen
Wilhelma is a renowned zoological and botanical garden in Stuttgart, Germany, known for its extensive animal and plant collections and historic Moorish-style architecture.
-
B.
Riedergarten
Riedergarten is a historic public garden and popular green oasis located in the Bavarian city of Rosenheim, Germany.
-
C.
Hohberg
Hohberg is a municipality in the Ortenau district of Baden-Württemberg in southwestern Germany.
-
D.
Gerhardine
Gerhardine is the given name of Gerdy Troost, a German architect and interior designer known for her close professional association with the Nazi regime.
-
E.
Berta
Berta is the sharp-tongued, no-nonsense housekeeper known for her sarcastic humor on the sitcom "Two and a Half Men."
- 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_69d806aabde48190899e13e41659cae5 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98bd1fc408190b4b5ca973bcee403 |
completed | April 10, 2026, 11:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6eaea3f888190b47ed7bf1c52e7e7 |
completed | May 3, 2026, 6:27 a.m. |
Created at: April 9, 2026, 9:11 p.m.