Triple
T30559509
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Manchester city centre road network |
E777796
|
entity |
| Predicate | includesRoad |
P85887
|
FINISHED |
| Object |
A665
A665 is a key A-road in Greater Manchester that forms part of the inner ring route around Manchester city centre.
|
E1922210
|
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: A665 | Statement: [Manchester city centre road network, includesRoad, A665]
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: A665 Triple: [Manchester city centre road network, includesRoad, A665]
Generated description
A665 is a key A-road in Greater Manchester that forms part of the inner ring route around Manchester city centre.
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_69f2249ed41c8190b175170ecfd6e1c5 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f688d85dd88190ba0b9ee798d44e89 |
completed | May 2, 2026, 11:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a2856f6d6108190b784d17331941139 |
completed | June 9, 2026, 6:09 p.m. |
| NEDg | Description generation | batch_6a2858941f488190b44e942eed9a57e6 |
completed | June 9, 2026, 6:16 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a28593b6d588190ac80f643ecd8efeb |
completed | June 9, 2026, 6:19 p.m. |
Created at: April 29, 2026, 8:21 p.m.