Triple
T6996731
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Henry Haller |
E162233
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Haller |
E306154
|
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: Haller | Statement: [Henry Haller, familyName, Haller]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Haller Context triple: [Henry Haller, familyName, Haller]
-
A.
Haller
chosen
Haller is a surname most notably associated with Ernest Haller, an American cinematographer renowned for his work in classic Hollywood films.
-
B.
Erasbach
Erasbach is a small locality in Bavaria, Germany, best known as the birthplace of the composer Christoph Willibald Gluck.
-
C.
Hohberg
Hohberg is a municipality in the Ortenau district of Baden-Württemberg in southwestern Germany.
-
D.
Haldenstein
Haldenstein is a small Swiss village in the canton of Graubünden, known in architecture circles as the longtime base of renowned architect Peter Zumthor.
-
E.
Hallenberg
Hallenberg is a small town and municipality in the Hochsauerland district of North Rhine-Westphalia, Germany, known for its scenic location in the Sauerland region.
- 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_69c68857ffc08190857dc62cd5253777 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6dbedafa48190af0d2b47e3a1e17e |
completed | March 27, 2026, 7:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c76a2465908190b69454f6215365b0 |
completed | March 28, 2026, 5:41 a.m. |
Created at: March 27, 2026, 2:32 p.m.