Triple
T10706383
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paris Métro Line 11 |
E252416
|
entity |
| Predicate | servesStation |
P839
|
FINISHED |
| Object | Pyrénées |
E7087
|
NE 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: Pyrénées | Statement: [Paris Métro Line 11, servesStation, Pyrénées]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pyrénées Context triple: [Paris Métro Line 11, servesStation, Pyrénées]
-
A.
Pyrenees
chosen
The Pyrenees are a major mountain range in southwest Europe forming a natural border between France and Spain, known for their rugged peaks, scenic valleys, and popular hiking and skiing areas.
-
B.
Sierra de Francia
Sierra de Francia is a mountainous subrange in western Spain known for its rugged landscapes, traditional villages, and rich natural and cultural heritage.
-
C.
Montes Alpes
Montes Alpes is a prominent lunar mountain range located along the northeastern edge of Mare Imbrium on the Moon.
-
D.
Midi-Pyrénées
Midi-Pyrénées was a former administrative region in southwestern France, centered on Toulouse and known for its diverse landscapes and rich Occitan cultural heritage.
-
E.
Montagne Noire
Montagne Noire is a mountain range in southern France known for its forested slopes, rich water resources, and role as a key watershed feeding regional rivers and canals.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d6aa5cbabc8190973e683950d89faf |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6fddfbed48190810bb3faee473fde |
completed | April 9, 2026, 1:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69dbd9cef8a48190a0ec4a27d5702e73 |
completed | April 12, 2026, 5:43 p.m. |
Created at: April 8, 2026, 9:12 p.m.