Triple
T145560
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hawaii |
E2945
|
entity |
| Predicate | postalCode |
P222
|
FINISHED |
| Object | HI |
E17028
|
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: HI | Statement: [Hawaii, postalCode, HI]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: HI Context triple: [Hawaii, postalCode, HI]
-
A.
HI
chosen
HI is the standard two-letter U.S. postal abbreviation for the state of Hawaii.
-
B.
IN
IN is the two-letter ISO 3166-1 alpha-2 country code representing India in international standards and systems.
-
C.
Hay
Hay is a rural service town in the Riverina region of southwestern New South Wales, Australia, known for its agricultural production and historic role as a transport and wool-growing center.
-
D.
IHR
IHR refers to the International Health Regulations, a legally binding framework coordinated by the World Health Organization to prevent and respond to the international spread of disease.
-
E.
HUP
HUP is a major academic medical center in Philadelphia that serves as the flagship teaching hospital of the University of Pennsylvania's health system.
- 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_69a2521e35c08190b28e5c9f1e3c9b59 |
completed | Feb. 28, 2026, 2:25 a.m. |
| NER | Named-entity recognition | batch_69a257ea7eac8190884a53453a9e0dd6 |
completed | Feb. 28, 2026, 2:50 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a2c2763ce481908c12046de9003a84 |
completed | Feb. 28, 2026, 10:24 a.m. |
Created at: Feb. 28, 2026, 2:31 a.m.