Triple
T13314503
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lleida Pirineus railway station |
E317154
|
entity |
| Predicate | hasIneCode |
P109469
|
FINISHED |
| Object | 25120 |
—
|
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: 25120 | Statement: [Lleida Pirineus railway station, hasIneCode, 25120]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasIneCode Context triple: [Lleida Pirineus railway station, hasIneCode, 25120]
-
A.
hasCodeIn
Indicates that one entity is represented, defined, or implemented within the codebase or coding context of another entity.
-
B.
hasProgramCode
Indicates that an entity is associated with a specific program identifier or code used to reference or classify it within a system.
-
C.
hasFeatureCode
Indicates that an entity is associated with a specific feature identifier or code that characterizes one of its properties or attributes.
-
D.
hasINSEECODE
Indicates that an entity is associated with a specific INSEE code, identifying it within the French national statistical and administrative system.
-
E.
hasONSCode
Indicates that an entity is associated with a specific code assigned by the Office for National Statistics (ONS) for identification or classification purposes.
- 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_69d806b4d62c81908d4ced1665414be5 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d99cfdc9388190af1fdd3cd4717bd8 |
completed | April 11, 2026, 12:59 a.m. |
| PD | Predicate disambiguation | batch_69d98f6babd88190a5d529df9584b9a4 |
completed | April 11, 2026, 12:01 a.m. |
| PDg | Predicate description generation | batch_69d99cf7f9c48190a6a4f452b4a2aefa |
completed | April 11, 2026, 12:59 a.m. |
Created at: April 9, 2026, 9:29 p.m.