Triple
T36022597
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Harrow and Wealdstone rail crash |
E1042028
|
entity |
| Predicate | rankByFatalitiesInUK |
P126019
|
FINISHED |
| Object | one of the worst |
—
|
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: one of the worst | Statement: [Harrow and Wealdstone rail crash, rankByFatalitiesInUK, one of the worst]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankByFatalitiesInUK Context triple: [Harrow and Wealdstone rail crash, rankByFatalitiesInUK, one of the worst]
-
A.
depthRankInBritishIsles
Indicates the relative ordering of an entity by depth compared to other entities within the British Isles.
-
B.
hasPopulationRankInUK
Indicates the relative position of an entity’s population size compared to other entities within the United Kingdom.
-
C.
rankingInUnitedKingdomByHeight
Indicates the position of an entity in an ordered list based on its height within the United Kingdom.
-
D.
notableDeathTollEvent
chosen
Indicates that an event is characterized by causing an unusually large or historically significant number of deaths.
-
E.
countryRankByDamage
Indicates the relative position of a country in an ordered list based on the amount of damage it has caused or received.
- F. None of above.
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_69f76e2c568881909e1e21f85252b0f0 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7ace4902c8190b4f60da85030a47e |
completed | May 3, 2026, 8:15 p.m. |
| PD | Predicate disambiguation | batch_69f7ab75387c819091afc3c2128eb903 |
completed | May 3, 2026, 8:09 p.m. |
Created at: May 3, 2026, 4:07 p.m.