Triple
T20101088
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Line 2 (Lille Metro) |
E496538
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Mercure
Mercure is a station on the Lille Metro system in northern France, serving passengers on Line 2.
|
E1411686
|
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: Mercure | Statement: [Line 2 (Lille Metro), hasStation, Mercure]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mercure Context triple: [Line 2 (Lille Metro), hasStation, Mercure]
-
A.
Mercure
Mercure is the Roman god of commerce, communication, and travel, often depicted as a swift messenger of the gods.
-
B.
Céreste
Céreste is a small commune in southeastern France’s Alpes-de-Haute-Provence department, known for its picturesque Provençal landscape and historic village setting.
-
C.
Meride
Meride is a small village in the canton of Ticino in southern Switzerland, known for its scenic location on Monte San Giorgio, a UNESCO World Heritage site famed for its fossil-rich geological formations.
-
D.
Morée
Morée is a small commune in the Loir-et-Cher department of central France, known for its rural character and location within the Vendôme arrondissement.
-
E.
Masonna
Masonna is a Japanese noise music project by Maso Yamazaki, known for its extremely harsh sound, chaotic performances, and influential role in the global noise scene.
- 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: Mercure Triple: [Line 2 (Lille Metro), hasStation, Mercure]
Generated description
Mercure is a station on the Lille Metro system in northern France, serving passengers on Line 2.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mercure Target entity description: Mercure is a station on the Lille Metro system in northern France, serving passengers on Line 2.
-
A.
Mercure
Mercure is the Roman god of commerce, communication, and travel, often depicted as a swift messenger of the gods.
-
B.
Céreste
Céreste is a small commune in southeastern France’s Alpes-de-Haute-Provence department, known for its picturesque Provençal landscape and historic village setting.
-
C.
Meride
Meride is a small village in the canton of Ticino in southern Switzerland, known for its scenic location on Monte San Giorgio, a UNESCO World Heritage site famed for its fossil-rich geological formations.
-
D.
Morée
Morée is a small commune in the Loir-et-Cher department of central France, known for its rural character and location within the Vendôme arrondissement.
-
E.
Masonna
Masonna is a Japanese noise music project by Maso Yamazaki, known for its extremely harsh sound, chaotic performances, and influential role in the global noise scene.
- 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_69da626eee3881909f3454986d4a6511 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e6666ff4008190ae1eec907c89bd3b |
completed | April 20, 2026, 5:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0827109f0081909747fb28dc439f77 |
completed | May 16, 2026, 8:13 a.m. |
| NEDg | Description generation | batch_6a0829a3a35881909505c9927563e646 |
completed | May 16, 2026, 8:24 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a082a26bff48190ab78612dfebc99d9 |
completed | May 16, 2026, 8:26 a.m. |
Created at: April 11, 2026, 11:26 p.m.