Triple
T2380819
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | State of Michoacán |
E46305
|
entity |
| Predicate | containsTown |
P847
|
FINISHED |
| Object |
Paracho
Paracho is a town in the Mexican state of Michoacán renowned for its traditional handcrafted guitars and vibrant luthier culture.
|
E261628
|
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: Paracho | Statement: [State of Michoacán, containsTown, Paracho]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Paracho Context triple: [State of Michoacán, containsTown, Paracho]
-
A.
Tacuba
Tacuba is a historic neighborhood in Mexico City known for its colonial-era architecture and role as a former pre-Hispanic town.
-
B.
Copilco
Copilco is a station on Mexico City’s Metro system, serving the Copilco neighborhood near the National Autonomous University of Mexico (UNAM).
-
C.
Ocotlán
Ocotlán is a city in the Mexican state of Jalisco, known for its furniture industry, religious traditions, and location near Lake Chapala.
-
D.
Ciudad Guzmán
Ciudad Guzmán is a city in western Mexico known as an important agricultural and commercial center in the southern region of the state of Jalisco.
-
E.
Tafoya
Tafoya is the surname of Michele Tafoya, a prominent American sportscaster best known for her work as an NFL sideline reporter.
- 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: Paracho Triple: [State of Michoacán, containsTown, Paracho]
Generated description
Paracho is a town in the Mexican state of Michoacán renowned for its traditional handcrafted guitars and vibrant luthier culture.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Paracho Target entity description: Paracho is a town in the Mexican state of Michoacán renowned for its traditional handcrafted guitars and vibrant luthier culture.
-
A.
Tacuba
Tacuba is a historic neighborhood in Mexico City known for its colonial-era architecture and role as a former pre-Hispanic town.
-
B.
Copilco
Copilco is a station on Mexico City’s Metro system, serving the Copilco neighborhood near the National Autonomous University of Mexico (UNAM).
-
C.
Ocotlán
Ocotlán is a city in the Mexican state of Jalisco, known for its furniture industry, religious traditions, and location near Lake Chapala.
-
D.
Ciudad Guzmán
Ciudad Guzmán is a city in western Mexico known as an important agricultural and commercial center in the southern region of the state of Jalisco.
-
E.
Tafoya
Tafoya is the surname of Michele Tafoya, a prominent American sportscaster best known for her work as an NFL sideline reporter.
- 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_69a88a1554a48190a0180682bcf099be |
completed | March 4, 2026, 7:37 p.m. |
| NER | Named-entity recognition | batch_69abc7b7c9188190a824e4b469bc1548 |
completed | March 7, 2026, 6:37 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69aea8b4f85c81909e5a4eda271b73ca |
completed | March 9, 2026, 11:02 a.m. |
| NEDg | Description generation | batch_69aeac747e488190aea9b1831ea748a8 |
completed | March 9, 2026, 11:18 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69aeadb6008481909bf1e5d1210a58b8 |
completed | March 9, 2026, 11:23 a.m. |
Created at: March 4, 2026, 7:57 p.m.