Triple
T5834949
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | London Midland |
E129443
|
entity |
| Predicate | franchiseEndYear |
P67461
|
FINISHED |
| Object | 2017 |
—
|
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: 2017 | Statement: [London Midland, franchiseEndYear, 2017]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: franchiseEndYear Context triple: [London Midland, franchiseEndYear, 2017]
-
A.
foundedAsFranchiseYear
Indicates the year in which an entity was established specifically as a franchise.
-
B.
franchiseDebut
Indicates the first appearance or introduction of an entity within a particular franchise or series.
-
C.
franchiseEra
Indicates the specific historical or operational period within which a franchise’s activities, rights, or identity are defined.
-
D.
franchiseStartSeason
Indicates the season in which a particular franchise or series first began or was introduced.
-
E.
appliedToFranchiseFoundedIn
Indicates that something (such as a rule, policy, or action) is applied specifically to a franchise that was founded in a particular time or place.
- 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_69c0084af79c81908af128ccc29983d0 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c044ab0a048190b84be40fb13c0f50 |
completed | March 22, 2026, 7:36 p.m. |
| PD | Predicate disambiguation | batch_69c03341e5888190a5f219b6f92cb161 |
completed | March 22, 2026, 6:21 p.m. |
| PDg | Predicate description generation | batch_69c044a9c4f0819081b8c196932883f6 |
completed | March 22, 2026, 7:36 p.m. |
Created at: March 22, 2026, 3:54 p.m.