Triple
T16173292
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Estagel |
E392495
|
entity |
| Predicate | hasRailwayStation |
P918
|
FINISHED |
| Object |
Estagel station
Estagel station is a small regional railway stop serving the commune of Estagel in southern France.
|
E1197648
|
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: Estagel station | Statement: [Estagel, hasRailwayStation, Estagel station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Estagel station Context triple: [Estagel, hasRailwayStation, Estagel station]
-
A.
Oriente station
Oriente station is a major multimodal transport hub in Lisbon, Portugal, serving as a key connection point for trains, metro, buses, and regional services.
-
B.
Hankar station
Hankar station is a Brussels Metro station on the city's Line 5, serving the Auderghem municipality in southeastern Brussels.
-
C.
Legarda station
Legarda station is an elevated rapid transit stop on Manila’s LRT Line 2 serving the Sampaloc area and nearby universities.
-
D.
Recreo station
Recreo station is a passenger rail station on the Valparaíso Metro system in Chile, serving the coastal area between Valparaíso and Viña del Mar.
-
E.
Imbiah station
Imbiah station is a monorail station on Singapore’s Sentosa Island serving the Imbiah attractions area along the Sentosa Express line.
- 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: Estagel station Triple: [Estagel, hasRailwayStation, Estagel station]
Generated description
Estagel station is a small regional railway stop serving the commune of Estagel in southern France.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Estagel station Target entity description: Estagel station is a small regional railway stop serving the commune of Estagel in southern France.
-
A.
Oriente station
Oriente station is a major multimodal transport hub in Lisbon, Portugal, serving as a key connection point for trains, metro, buses, and regional services.
-
B.
Hankar station
Hankar station is a Brussels Metro station on the city's Line 5, serving the Auderghem municipality in southeastern Brussels.
-
C.
Legarda station
Legarda station is an elevated rapid transit stop on Manila’s LRT Line 2 serving the Sampaloc area and nearby universities.
-
D.
Recreo station
Recreo station is a passenger rail station on the Valparaíso Metro system in Chile, serving the coastal area between Valparaíso and Viña del Mar.
-
E.
Imbiah station
Imbiah station is a monorail station on Singapore’s Sentosa Island serving the Imbiah attractions area along the Sentosa Express line.
- 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_69d87f1d32208190942e4e499a80c18c |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e21eb9b8208190b60874cec7a3a98e |
completed | April 17, 2026, 11:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fff7bfc3ac819082596cc533c5faa4 |
completed | May 10, 2026, 3:13 a.m. |
| NEDg | Description generation | batch_69fff8bc4f7c81908f7e9ffaa9f3cfb1 |
completed | May 10, 2026, 3:17 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fff94cd32081908205ae383e58d148 |
completed | May 10, 2026, 3:19 a.m. |
Created at: April 10, 2026, 5:02 a.m.