Triple
T10234847
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hull railway station |
E243437
|
entity |
| Predicate | stationCode |
P1289
|
FINISHED |
| Object | HUL |
E436043
|
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: HUL | Statement: [Hull railway station, stationCode, HUL]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: HUL Context triple: [Hull railway station, stationCode, HUL]
-
A.
HUL
chosen
HUL is the National Rail station code for Hull Paragon Interchange, the main railway and bus station in Kingston upon Hull, England.
-
B.
Henkel
Henkel is a German multinational chemical and consumer goods company best known for its brands in laundry, home care, and adhesives.
-
C.
Procter & Gamble
Procter & Gamble is a multinational consumer goods corporation known for a wide range of household, personal care, and hygiene brands sold globally.
-
D.
Unilever
Unilever is a multinational consumer goods company known for its wide range of food, personal care, and household products sold globally.
-
E.
Lifebuoy
Lifebuoy is a long-established global soap and hygiene brand known for its antibacterial products and health-focused marketing.
- 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_69d381b0f97c819085c9b45799a5fb7c |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d20cd8708190ba42752597d62008 |
completed | April 7, 2026, 9:44 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d6f757b514819087f5d5f659c50c66 |
completed | April 9, 2026, 12:48 a.m. |
Created at: April 6, 2026, 11:21 a.m.