Triple
T24715646
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Clarehall |
E612150
|
entity |
| Predicate | hasNearbyShoppingCentre |
P5648
|
FINISHED |
| Object |
Clarehall Shopping Centre
Clarehall Shopping Centre is a local retail complex serving the Clarehall area with a range of shops and services.
|
E1647247
|
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: Clarehall Shopping Centre | Statement: [Clarehall, hasNearbyShoppingCentre, Clarehall Shopping Centre]
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: Clarehall Shopping Centre Triple: [Clarehall, hasNearbyShoppingCentre, Clarehall Shopping Centre]
Generated description
Clarehall Shopping Centre is a local retail complex serving the Clarehall area with a range of shops and services.
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_69e2d7d6e7a48190bb43b0d8bb1137a0 |
completed | April 18, 2026, 1:01 a.m. |
| NER | Named-entity recognition | batch_69f41012add48190a37f9fbc76822c39 |
completed | May 1, 2026, 2:29 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a101013220881908d035b4c37749f51 |
completed | May 22, 2026, 8:13 a.m. |
| NEDg | Description generation | batch_6a10136d2c448190918a7eeb751a2a84 |
completed | May 22, 2026, 8:27 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a101436b0008190a5e27291df640af5 |
completed | May 22, 2026, 8:30 a.m. |
Created at: April 18, 2026, 3:36 a.m.