Triple
T1986088
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Göttingen railway station |
E43143
|
entity |
| Predicate | hasIBNRCode |
P1289
|
FINISHED |
| Object | 8000128 |
—
|
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: 8000128 | Statement: [Göttingen railway station, hasIBNRCode, 8000128]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasIBNRCode Context triple: [Göttingen railway station, hasIBNRCode, 8000128]
-
A.
hasIATAcode
Indicates that an entity, typically a transportation facility like an airport, is associated with a specific IATA (International Air Transport Association) code.
-
B.
hasATCCode
Indicates that a pharmaceutical product or substance is assigned a specific Anatomical Therapeutic Chemical (ATC) classification code.
-
C.
hasStationCode
chosen
Indicates that an entity is associated with a specific station identification code.
-
D.
hasINSEECODE
Indicates that an entity is associated with a specific INSEE code, identifying it within the French national statistical and administrative system.
-
E.
hasISIN
Indicates that a financial instrument is associated with a specific International Securities Identification Number (ISIN) that uniquely identifies it.
- 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_69a88713ddc88190a969715658ebe7a8 |
completed | March 4, 2026, 7:25 p.m. |
| NER | Named-entity recognition | batch_69abb96f932881908bebfc4176fda7c0 |
completed | March 7, 2026, 5:36 a.m. |
| PD | Predicate disambiguation | batch_69abb798d288819083132cf14605bd02 |
completed | March 7, 2026, 5:28 a.m. |
Created at: March 4, 2026, 7:37 p.m.