Triple
T12879702
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GR footpath network |
E308058
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object |
GR 12
GR 12 is a long-distance hiking trail that forms part of the GR footpath network in Europe.
|
E1007822
|
NE FINISHED |
How this triple was built (4 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: GR 12 | Statement: [GR footpath network, hasPart, GR 12]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: GR 12 Context triple: [GR footpath network, hasPart, GR 12]
-
A.
GR 11
GR 11 is a long-distance hiking trail that traverses the Spanish side of the Pyrenees from the Atlantic Ocean to the Mediterranean Sea.
-
B.
GR 10
GR 10 is a long-distance hiking trail that traverses the French side of the Pyrenees from the Atlantic Ocean to the Mediterranean Sea.
-
C.
HSC
HSC is the abbreviation for the Harmonized System Committee, the international body responsible for overseeing and updating the Harmonized Commodity Description and Coding System used in global trade.
-
D.
HSC
HSC is the three-letter station code used to identify High Street Kensington Underground station on the London Underground network.
-
E.
HSC
HSC is the publicly funded health and social care system in Northern Ireland that provides integrated medical and social services to residents.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: GR 12 Triple: [GR footpath network, hasPart, GR 12]
Generated description
GR 12 is a long-distance hiking trail that forms part of the GR footpath network in Europe.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: GR 12 Target entity description: GR 12 is a long-distance hiking trail that forms part of the GR footpath network in Europe.
-
A.
GR 11
GR 11 is a long-distance hiking trail that traverses the Spanish side of the Pyrenees from the Atlantic Ocean to the Mediterranean Sea.
-
B.
GR 10
GR 10 is a long-distance hiking trail that traverses the French side of the Pyrenees from the Atlantic Ocean to the Mediterranean Sea.
-
C.
HSC
HSC is the abbreviation for the Harmonized System Committee, the international body responsible for overseeing and updating the Harmonized Commodity Description and Coding System used in global trade.
-
D.
HSC
HSC is the three-letter station code used to identify High Street Kensington Underground station on the London Underground network.
-
E.
HSC
HSC is the publicly funded health and social care system in Northern Ireland that provides integrated medical and social services to residents.
- F. None of above. chosen
Provenance (5 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_69d7bdf69bc48190af6c2621f28ca351 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d970fc1e488190a0c48039f6213e62 |
completed | April 10, 2026, 9:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f69bba33c081909c0050ff7b868a8e |
completed | May 3, 2026, 12:50 a.m. |
| NEDg | Description generation | batch_69f69df1032881909255e506ddfd9c9f |
completed | May 3, 2026, 12:59 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f69ea645a0819095edc112b9ed9bff |
completed | May 3, 2026, 1:02 a.m. |
Created at: April 9, 2026, 5:39 p.m.