Triple
T1788193
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paris Métro Line 5 |
E39435
|
entity |
| Predicate | servesStation |
P839
|
FINISHED |
| Object |
Hoche
Hoche is a Paris Métro station located in the northeastern suburb of Pantin, serving as a stop on the city’s Line 5.
|
E204487
|
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: Hoche | Statement: [Paris Métro Line 5, servesStation, Hoche]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hoche Context triple: [Paris Métro Line 5, servesStation, Hoche]
-
A.
Brocken
Brocken is a prominent mountain in central Germany’s Harz range, known for its harsh climate, folklore, and role in literature and cultural history.
-
B.
Mount Eisen
Mount Eisen is a peak in California’s Sierra Nevada range, situated along the rugged Great Western Divide within Sequoia National Park.
-
C.
Alsberg
Alsberg is a surname of Germanic origin borne by various notable individuals, including American writer and theater director Henry Alsberg.
-
D.
Wilseder Berg
Wilseder Berg is a prominent hill and popular viewpoint in northern Germany, known for its scenic heathland landscapes within the Lüneburg Heath region.
-
E.
Nadelhorn
Nadelhorn is a prominent 4,000-meter-class peak in the Swiss Alps, known for its sharp, needle-like summit and popular alpine climbing routes.
- 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: Hoche Triple: [Paris Métro Line 5, servesStation, Hoche]
Generated description
Hoche is a Paris Métro station located in the northeastern suburb of Pantin, serving as a stop on the city’s Line 5.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hoche Target entity description: Hoche is a Paris Métro station located in the northeastern suburb of Pantin, serving as a stop on the city’s Line 5.
-
A.
Brocken
Brocken is a prominent mountain in central Germany’s Harz range, known for its harsh climate, folklore, and role in literature and cultural history.
-
B.
Mount Eisen
Mount Eisen is a peak in California’s Sierra Nevada range, situated along the rugged Great Western Divide within Sequoia National Park.
-
C.
Alsberg
Alsberg is a surname of Germanic origin borne by various notable individuals, including American writer and theater director Henry Alsberg.
-
D.
Wilseder Berg
Wilseder Berg is a prominent hill and popular viewpoint in northern Germany, known for its scenic heathland landscapes within the Lüneburg Heath region.
-
E.
Nadelhorn
Nadelhorn is a prominent 4,000-meter-class peak in the Swiss Alps, known for its sharp, needle-like summit and popular alpine climbing routes.
- 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_69a88631854081909723959921e45c2b |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69aa650fd3448190a6a2c979db982cae |
completed | March 6, 2026, 5:24 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adbf52266081909b7478d76664d387 |
completed | March 8, 2026, 6:26 p.m. |
| NEDg | Description generation | batch_69adc0a3fdd88190b0ffa98db1b5cf80 |
completed | March 8, 2026, 6:32 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69adc1304a808190a999e71dfa39162a |
completed | March 8, 2026, 6:34 p.m. |
Created at: March 4, 2026, 7:32 p.m.