Triple
T5066419
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Haabersti |
E114153
|
entity |
| Predicate | hasInstitution |
P186
|
FINISHED |
| Object | Tallinn Zoo |
E490019
|
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: Tallinn Zoo | Statement: [Haabersti, hasInstitution, Tallinn Zoo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tallinn Zoo Context triple: [Haabersti, hasInstitution, Tallinn Zoo]
-
A.
Tallinn Zoo
chosen
Tallinn Zoo is a major zoological park in Tallinn, Estonia, known for its diverse collection of animal species and active conservation and education programs.
-
B.
Odense Zoo
Odense Zoo is a popular Danish zoological garden in the city of Odense, known for its diverse animal collections and family-friendly exhibits.
-
C.
Ostrava Zoo
Ostrava Zoo is a zoological garden in Ostrava, Czech Republic, known for its extensive collection of animal species and large naturalistic enclosures.
-
D.
Plzeň Zoo
Plzeň Zoo is a major zoological garden in the Czech city of Plzeň, known for its diverse animal collection and role in conservation and education.
-
E.
Novosibirsk Zoo
Novosibirsk Zoo is a major Russian zoological park renowned for its extensive collection of animal species and successful breeding programs for rare and endangered animals.
- 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_69bd443c0c8c81908663b77afb28e165 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd749aceac8190817278266308fd64 |
completed | March 20, 2026, 4:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69beb10778208190a5c6a9457c085491 |
completed | March 21, 2026, 2:53 p.m. |
Created at: March 20, 2026, 1:38 p.m.