Triple
T38112694
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brass Local Government Area |
E951697
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object |
Twon-Brass
Twon-Brass is a coastal settlement in Bayelsa State, Nigeria, known for its location in the Niger Delta and its role in local fishing and oil-related activities.
|
E2255792
|
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: Twon-Brass | Statement: [Brass Local Government Area, hasSettlement, Twon-Brass]
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: Twon-Brass Triple: [Brass Local Government Area, hasSettlement, Twon-Brass]
Generated description
Twon-Brass is a coastal settlement in Bayelsa State, Nigeria, known for its location in the Niger Delta and its role in local fishing and oil-related activities.
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_69f76f065ed08190bdfb1b6d817f5b39 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fc45c103c88190bdf42523bde7dd6d |
completed | May 7, 2026, 7:56 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a41681bcb1c819080c0d01847dbf71a |
completed | June 28, 2026, 6:29 p.m. |
| NEDg | Description generation | batch_6a4169348974819084f87c65d760bcfc |
completed | June 28, 2026, 6:34 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a416a50e8e48190bdca9011f38d6436 |
completed | June 28, 2026, 6:39 p.m. |
Created at: May 3, 2026, 4:21 p.m.