Triple
T24152242
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kilómetro 20 neighborhood |
E598567
|
entity |
| Predicate | hasNameInLanguage |
P15
|
FINISHED |
| Object |
Barrio Kilómetro 20
Barrio Kilómetro 20 is a small residential neighborhood, likely situated around the 20-kilometer mark along a main road or route in a Spanish-speaking region.
|
E1624424
|
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: Barrio Kilómetro 20 | Statement: [Kilómetro 20 neighborhood, hasNameInLanguage, Barrio Kilómetro 20]
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: Barrio Kilómetro 20 Triple: [Kilómetro 20 neighborhood, hasNameInLanguage, Barrio Kilómetro 20]
Generated description
Barrio Kilómetro 20 is a small residential neighborhood, likely situated around the 20-kilometer mark along a main road or route in a Spanish-speaking region.
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_69e288c9e488819093dd1acd91b08b8a |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f1e0e1e5748190bcc6681d409dcc05 |
completed | April 29, 2026, 10:43 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0fbd0839c88190a3fc9fa2c0c96108 |
completed | May 22, 2026, 2:18 a.m. |
| NEDg | Description generation | batch_6a0fbec907148190832159960dc4bdd6 |
completed | May 22, 2026, 2:26 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0fbf3cf7988190a9d766bfca4ef994 |
completed | May 22, 2026, 2:28 a.m. |
Created at: April 17, 2026, 11:30 p.m.