Triple
T24437873
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Schauspielhaus Zürich |
E616172
|
entity |
| Predicate | hasStage |
P2393
|
FINISHED |
| Object |
Schiffbau Box
Schiffbau Box is a smaller, flexible performance space within Zürich’s Schiffbau complex, used by Schauspielhaus Zürich for contemporary and experimental theatre productions.
|
E1634622
|
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: Schiffbau Box | Statement: [Schauspielhaus Zürich, hasStage, Schiffbau Box]
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Schiffbau Box Triple: [Schauspielhaus Zürich, hasStage, Schiffbau Box]
Generated description
Schiffbau Box is a smaller, flexible performance space within Zürich’s Schiffbau complex, used by Schauspielhaus Zürich for contemporary and experimental theatre productions.
Provenance (5 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_69e2d7ec44b081909ccaf1f3bbec0641 |
completed | April 18, 2026, 1:01 a.m. |
| NER | Named-entity recognition | batch_69f297881fa08190b82bdc5ebeae96f7 |
completed | April 29, 2026, 11:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0fe37d43948190a86515fd7fabaf50 |
completed | May 22, 2026, 5:02 a.m. |
| NEDg | Description generation | batch_6a0fe44e2f9c8190a16f81052341c70a |
completed | May 22, 2026, 5:06 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0fe4e5d698819092a5d1b75f213ca0 |
completed | May 22, 2026, 5:08 a.m. |
Created at: April 18, 2026, 2:16 a.m.