Triple
T4298851
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hitchin |
E99782
|
entity |
| Predicate | hasPostcodeArea |
P920
|
FINISHED |
| Object | SG |
E209989
|
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: SG | Statement: [Hitchin, hasPostcodeArea, SG]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SG Context triple: [Hitchin, hasPostcodeArea, SG]
-
A.
SG
chosen
SG is a postcode area in the United Kingdom covering parts of Hertfordshire and surrounding regions.
-
B.
SG
SG is the Secretariat-General of the European Commission, the central administrative body that supports the Commission’s work, coordination, and decision-making processes.
-
C.
SV
SV is the two-letter ISO 3166-1 alpha-2 country code assigned to El Salvador.
-
D.
SV
SV is the official abbreviation for the Slovenian Armed Forces, the military organization responsible for defending the Republic of Slovenia.
-
E.
SV
SV is the commonly used abbreviation for the Faculty of Social Sciences at the University of Oslo, encompassing disciplines such as sociology, political science, economics, and related fields.
- 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_69b3455175088190aa79c6e03b86647e |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3509d39348190aa83304661230cba |
completed | March 12, 2026, 11:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5c746c9108190a1a81e94b4768f3c |
completed | March 14, 2026, 8:38 p.m. |
Created at: March 12, 2026, 11:08 p.m.