Triple
T24751251
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Santo Domingo Province |
E619149
|
entity |
| Predicate | adjacentTo |
P224
|
FINISHED |
| Object |
San Cristóbal Province
San Cristóbal Province is an administrative region in the southern Dominican Republic known for its historical significance, industrial activity, and proximity to the national capital.
|
E1655732
|
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: San Cristóbal Province | Statement: [Santo Domingo Province, adjacentTo, San Cristóbal Province]
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: San Cristóbal Province Triple: [Santo Domingo Province, adjacentTo, San Cristóbal Province]
Generated description
San Cristóbal Province is an administrative region in the southern Dominican Republic known for its historical significance, industrial activity, and proximity to the national capital.
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_69e2fabb349881908a13a212a0221a63 |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f410757b6881908f79b56a143d78be |
completed | May 1, 2026, 2:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a1032fd3d5081908313684c98e37caa |
completed | May 22, 2026, 10:42 a.m. |
| NEDg | Description generation | batch_6a1033ece8248190bc0ee7fa4976848d |
completed | May 22, 2026, 10:46 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a10348fb55c819087a28d4a7280589c |
completed | May 22, 2026, 10:48 a.m. |
Created at: April 18, 2026, 4:24 a.m.