Triple
T7532424
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sierra de Tapalpa |
E178057
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object |
Tapalpa
Tapalpa is a picturesque mountain town in the Mexican state of Jalisco, known for its colonial architecture, pine forests, and outdoor recreation.
|
E670898
|
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: Tapalpa | Statement: [Sierra de Tapalpa, near, Tapalpa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tapalpa Context triple: [Sierra de Tapalpa, near, Tapalpa]
-
A.
Tapaz
Tapaz is a landlocked agricultural municipality in the province of Capiz on Panay Island in the Philippines, known for its rural landscapes and river valleys.
-
B.
Zapota
Zapota is a metro station on Mexico City’s Line 12, serving passengers in the southeastern part of the city.
-
C.
Tobalaba
Tobalaba is a major Santiago Metro station in Chile that serves as an important transfer point between multiple lines in the city’s rapid transit network.
-
D.
Tiendesitas
Tiendesitas is a popular shopping and lifestyle complex in Pasig, Metro Manila, known for its Filipino-themed architecture, handicrafts, food, and live entertainment.
-
E.
Tuktukan
Tuktukan is a barangay (village-level administrative division) in the city of Taguig in Metro Manila, Philippines.
- 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: Tapalpa Triple: [Sierra de Tapalpa, near, Tapalpa]
Generated description
Tapalpa is a picturesque mountain town in the Mexican state of Jalisco, known for its colonial architecture, pine forests, and outdoor recreation.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tapalpa Target entity description: Tapalpa is a picturesque mountain town in the Mexican state of Jalisco, known for its colonial architecture, pine forests, and outdoor recreation.
-
A.
Tapaz
Tapaz is a landlocked agricultural municipality in the province of Capiz on Panay Island in the Philippines, known for its rural landscapes and river valleys.
-
B.
Zapota
Zapota is a metro station on Mexico City’s Line 12, serving passengers in the southeastern part of the city.
-
C.
Tobalaba
Tobalaba is a major Santiago Metro station in Chile that serves as an important transfer point between multiple lines in the city’s rapid transit network.
-
D.
Tiendesitas
Tiendesitas is a popular shopping and lifestyle complex in Pasig, Metro Manila, known for its Filipino-themed architecture, handicrafts, food, and live entertainment.
-
E.
Tuktukan
Tuktukan is a barangay (village-level administrative division) in the city of Taguig in Metro Manila, Philippines.
- 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_69c69f2acdbc8190b5a8320168c1d0ba |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f84753fc81908bee2013004ef5fb |
completed | March 27, 2026, 9:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c84f03485c8190829beb95a8be0236 |
completed | March 28, 2026, 9:58 p.m. |
| NEDg | Description generation | batch_69c84f9ce82081908813a1185b9b3570 |
completed | March 28, 2026, 10:01 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c8500e64788190b196988290aea7d7 |
completed | March 28, 2026, 10:02 p.m. |
Created at: March 27, 2026, 3:47 p.m.