Triple
T9732238
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paris Métro Line 3 |
E235772
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Pereire
Pereire is a Paris Métro station in the 17th arrondissement, serving as a stop on Line 3 and connecting to nearby RER services.
|
E816335
|
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: Pereire | Statement: [Paris Métro Line 3, hasStation, Pereire]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pereire Context triple: [Paris Métro Line 3, hasStation, Pereire]
-
A.
Noguès
Noguès is a French surname borne by various notable individuals, including figures in politics, arts, and sports.
-
B.
Sergi
Sergi is a masculine given name of Catalan origin, commonly used in Spain and other Catalan-speaking regions.
-
C.
Miquel
Miquel is a given name, commonly used in Catalan and other languages, that corresponds to the English name Michael.
-
D.
Pau Cristià
Pau Cristià, also known as Pablo Christiani, was a 13th-century Jewish convert to Christianity infamous for his role in anti-Jewish polemics and the Barcelona Disputation against Rabbi Nachmanides.
-
E.
Pagnerre
Pagnerre was a 19th-century French publishing house known for issuing major literary works, including those of Victor Hugo.
- 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: Pereire Triple: [Paris Métro Line 3, hasStation, Pereire]
Generated description
Pereire is a Paris Métro station in the 17th arrondissement, serving as a stop on Line 3 and connecting to nearby RER services.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Pereire Target entity description: Pereire is a Paris Métro station in the 17th arrondissement, serving as a stop on Line 3 and connecting to nearby RER services.
-
A.
Noguès
Noguès is a French surname borne by various notable individuals, including figures in politics, arts, and sports.
-
B.
Sergi
Sergi is a masculine given name of Catalan origin, commonly used in Spain and other Catalan-speaking regions.
-
C.
Miquel
Miquel is a given name, commonly used in Catalan and other languages, that corresponds to the English name Michael.
-
D.
Pau Cristià
Pau Cristià, also known as Pablo Christiani, was a 13th-century Jewish convert to Christianity infamous for his role in anti-Jewish polemics and the Barcelona Disputation against Rabbi Nachmanides.
-
E.
Pagnerre
Pagnerre was a 19th-century French publishing house known for issuing major literary works, including those of Victor Hugo.
- 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_69ca84d0fad481909cdd45aa77416c48 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9eb3d6e4819090b3c7fb92550c57 |
completed | April 1, 2026, 10:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d19fbbba2081909a15725a68423162 |
completed | April 4, 2026, 11:33 p.m. |
| NEDg | Description generation | batch_69d1a065ce008190985b792302daa7cb |
completed | April 4, 2026, 11:36 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d1a0f811fc8190b6a46a0441159089 |
completed | April 4, 2026, 11:38 p.m. |
Created at: March 30, 2026, 8:22 p.m.