Triple
T4720203
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Moston |
E104744
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Harpurhey |
E96876
|
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: Harpurhey | Statement: [Moston, locatedNear, Harpurhey]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Harpurhey Context triple: [Moston, locatedNear, Harpurhey]
-
A.
Harpurhey
chosen
Harpurhey is an inner-city district of Manchester, England, known for its dense residential areas and local shopping precincts.
-
B.
Zaria Local Government Area
Zaria Local Government Area is an administrative region in Kaduna State, Nigeria, centered on the historic city of Zaria, a major Hausa-Fulani cultural and commercial hub.
-
C.
Ikoyi
Ikoyi is an affluent, high-end residential and commercial district in Lagos, Nigeria, known for its luxury real estate, upscale hotels, and diplomatic presence.
-
D.
Lekki
Lekki is a fictional companion mascot character associated with Nokki, likely designed as a cute, supportive sidekick figure.
-
E.
Lekki
Lekki is the official mascot character created for the XVIII Olympic Winter Games.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69bd43ec4a348190bc41afae43375e71 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6428e9e081908ce4041183cad13b |
completed | March 20, 2026, 3:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be108fe3b08190b3d306ca4b39860d |
completed | March 21, 2026, 3:29 a.m. |
Created at: March 20, 2026, 1:18 p.m.