Triple
T6872005
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hertsmere |
E158570
|
entity |
| Predicate | hasONSCode |
P73848
|
FINISHED |
| Object | 26UB |
—
|
LITERAL 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: 26UB | Statement: [Hertsmere, hasONSCode, 26UB]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasONSCode Context triple: [Hertsmere, hasONSCode, 26UB]
-
A.
hasINSEECODE
Indicates that an entity is associated with a specific INSEE code, identifying it within the French national statistical and administrative system.
-
B.
hasSiteCode
Indicates that an entity is associated with a specific site identifier or code used to distinguish its location or facility.
-
C.
hasFeatureCode
Indicates that an entity is associated with a specific feature identifier or code that characterizes one of its properties or attributes.
-
D.
hasCategoryOn
Indicates that something is assigned to or associated with a specific category within a given context or scope.
-
E.
hasCP
Indicates that an entity possesses, is associated with, or is characterized by a specific CP (such as a control point, contact person, or configuration parameter), depending on the domain context.
- F. None of above. chosen
Provenance (4 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_69c68831e3648190a643c328122e4d43 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d8c73ea08190b6bb1463e7ead47b |
completed | March 27, 2026, 7:21 p.m. |
| PD | Predicate disambiguation | batch_69c6d7b363dc8190a7225b540ab2bc40 |
completed | March 27, 2026, 7:17 p.m. |
| PDg | Predicate description generation | batch_69c6d8c48ba48190b8d3aa7b8d22816b |
completed | March 27, 2026, 7:21 p.m. |
Created at: March 27, 2026, 2:22 p.m.