Triple
T31812542
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | HafenCity Universität |
E812044
|
entity |
| Predicate | hasEmergencyCallPoints |
P5951
|
FINISHED |
| Object | yes |
—
|
LITERAL 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: yes | Statement: [HafenCity Universität, hasEmergencyCallPoints, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEmergencyCallPoints Context triple: [HafenCity Universität, hasEmergencyCallPoints, yes]
-
A.
hasEmergencyServices
Indicates that the subject provides or is equipped with emergency response services (such as police, fire, or medical assistance).
-
B.
hasEmergencyServiceProvider
Indicates that an entity is associated with or served by a specific emergency service provider (such as police, fire, or medical services).
-
C.
hasEmergencyOperationsCentres
Indicates that an entity possesses or is equipped with one or more emergency operations centres for managing crises or emergency situations.
-
D.
hasEmergencyIntercoms
chosen
Indicates that an entity is equipped with emergency intercom devices available for use in urgent or crisis situations.
-
E.
hasEmergencyAlarm
Indicates that an entity is equipped with or associated with an emergency alarm system that can be activated in urgent situations.
- F. None of above.
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_69f348e846c081908eb468a0665afd55 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f6afebd7ec8190ab696f363d84abf0 |
completed | May 3, 2026, 2:16 a.m. |
| PD | Predicate disambiguation | batch_69f6aca3dedc81908b519d53d2909868 |
completed | May 3, 2026, 2:02 a.m. |
Created at: April 30, 2026, 11:44 p.m.