Triple
T8465251
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Würzburg Riese |
E200144
|
entity |
| Predicate | meaningOfName |
P1966
|
FINISHED |
| Object | Würzburg Giant |
E200144
|
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: Würzburg Giant | Statement: [Würzburg Riese, meaningOfName, Würzburg Giant]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Würzburg Giant Context triple: [Würzburg Riese, meaningOfName, Würzburg Giant]
-
A.
Würzburg Riese
chosen
Würzburg Riese was a larger, more powerful German World War II ground-based radar system used primarily for air defense and gun-laying.
-
B.
Krumnagel
Krumnagel is a lesser-known work by Peter Ustinov, reflecting his multifaceted career as a writer alongside his fame as an actor and director.
-
C.
Roter Turm
Roter Turm is a historic medieval tower and prominent architectural landmark in the city center of Chemnitz, Germany.
-
D.
Roter Turm
Roter Turm is a historic clock and bell tower in Halle (Saale), Germany, and one of the city’s most recognizable architectural landmarks.
-
E.
Taunusstein
Taunusstein is a town in the German state of Hesse, located in the scenic Taunus mountain region and known for its residential character and natural surroundings.
- 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_69ca83198c4c8190a337bf717d1813f5 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe4d05b2881909bddf58df0ee1143 |
completed | March 31, 2026, 3:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce39e0d7788190add03271c940e1ff |
completed | April 2, 2026, 9:41 a.m. |
Created at: March 30, 2026, 6:11 p.m.