Triple
T7632571
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anaga Rural Park |
E172791
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Taganana
Taganana is a historic coastal village on Tenerife in Spain’s Canary Islands, known for its dramatic cliffs, traditional architecture, and location within the Anaga mountain range.
|
E678821
|
NE FINISHED |
How this triple was built (4 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: Taganana | Statement: [Anaga Rural Park, contains, Taganana]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Taganana Context triple: [Anaga Rural Park, contains, Taganana]
-
A.
Yuriatin
Yuriatin is a fictional Russian town in Boris Pasternak’s novel "Doctor Zhivago," serving as a key setting in Yuri Zhivago’s life and relationships.
-
B.
The Don
The Don is the University of San Francisco’s Spanish-influenced, nobleman-themed athletic mascot representing the school’s sports teams.
-
C.
Gorky Park
Gorky Park is a famous central Moscow park known for its recreational facilities, cultural events, and scenic riverside location.
-
D.
Gorky Park
Gorky Park is a 1983 crime thriller film, based on Martin Cruz Smith’s novel, about a Soviet detective investigating a triple murder in Moscow.
-
E.
Mishenka
Mishenka is a Russian affectionate diminutive form of the male given name Mikhail.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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: Taganana Triple: [Anaga Rural Park, contains, Taganana]
Generated description
Taganana is a historic coastal village on Tenerife in Spain’s Canary Islands, known for its dramatic cliffs, traditional architecture, and location within the Anaga mountain range.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Taganana Target entity description: Taganana is a historic coastal village on Tenerife in Spain’s Canary Islands, known for its dramatic cliffs, traditional architecture, and location within the Anaga mountain range.
-
A.
Yuriatin
Yuriatin is a fictional Russian town in Boris Pasternak’s novel "Doctor Zhivago," serving as a key setting in Yuri Zhivago’s life and relationships.
-
B.
The Don
The Don is the University of San Francisco’s Spanish-influenced, nobleman-themed athletic mascot representing the school’s sports teams.
-
C.
Gorky Park
Gorky Park is a famous central Moscow park known for its recreational facilities, cultural events, and scenic riverside location.
-
D.
Gorky Park
Gorky Park is a 1983 crime thriller film, based on Martin Cruz Smith’s novel, about a Soviet detective investigating a triple murder in Moscow.
-
E.
Mishenka
Mishenka is a Russian affectionate diminutive form of the male given name Mikhail.
- F. None of above. chosen
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_69c69952849881908fdcea7a93bfc307 |
completed | March 27, 2026, 2:50 p.m. |
| NER | Named-entity recognition | batch_69c6faa5f4f08190a7e5259a1b6fb576 |
completed | March 27, 2026, 9:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c870b95c1c8190a7d885bd0a534a6a |
completed | March 29, 2026, 12:22 a.m. |
| NEDg | Description generation | batch_69c8723391f48190b60ba8952c9ccca7 |
completed | March 29, 2026, 12:28 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c87401aaa48190b3e44298fcd3f37f |
completed | March 29, 2026, 12:36 a.m. |
Created at: March 27, 2026, 3:57 p.m.