Triple
T2261657
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pessac-Léognan |
E50052
|
entity |
| Predicate | containsCommune |
P15149
|
FINISHED |
| Object |
Talence
Talence is a suburban commune in southwestern France, located near Bordeaux and known for hosting part of the University of Bordeaux campus.
|
E251261
|
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: Talence | Statement: [Pessac-Léognan, containsCommune, Talence]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Talence Context triple: [Pessac-Léognan, containsCommune, Talence]
-
A.
Troizen
Troizen is an ancient town in the northeastern Peloponnese of Greece, traditionally associated with the myths of Theseus and located within the region of Argolid.
-
B.
Nattheim
Nattheim is a municipality in the Heidenheim district of Baden-Württemberg in southern Germany.
-
C.
Valleiry
Valleiry is a small French commune in the Haute-Savoie department of the Auvergne-Rhône-Alpes region in southeastern France, near the Swiss border.
-
D.
Melle
Melle is a town in Lower Saxony, Germany, known for its rural character, historical architecture, and role as a regional economic center.
-
E.
Brackenheim
Brackenheim is a small town in the German state of Baden-Württemberg, best known as the birthplace of Theodor Heuss, the first President of the Federal Republic of Germany.
- 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: Talence Triple: [Pessac-Léognan, containsCommune, Talence]
Generated description
Talence is a suburban commune in southwestern France, located near Bordeaux and known for hosting part of the University of Bordeaux campus.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Talence Target entity description: Talence is a suburban commune in southwestern France, located near Bordeaux and known for hosting part of the University of Bordeaux campus.
-
A.
Troizen
Troizen is an ancient town in the northeastern Peloponnese of Greece, traditionally associated with the myths of Theseus and located within the region of Argolid.
-
B.
Nattheim
Nattheim is a municipality in the Heidenheim district of Baden-Württemberg in southern Germany.
-
C.
Valleiry
Valleiry is a small French commune in the Haute-Savoie department of the Auvergne-Rhône-Alpes region in southeastern France, near the Swiss border.
-
D.
Melle
Melle is a town in Lower Saxony, Germany, known for its rural character, historical architecture, and role as a regional economic center.
-
E.
Brackenheim
Brackenheim is a small town in the German state of Baden-Württemberg, best known as the birthplace of Theodor Heuss, the first President of the Federal Republic of Germany.
- 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_69a88b01e0048190ba96431b5f990ba9 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abc5b262488190b6455d1d28d2306d |
completed | March 7, 2026, 6:29 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae71cdacb48190bc11e9e0e6b61ba0 |
completed | March 9, 2026, 7:07 a.m. |
| NEDg | Description generation | batch_69ae727388f48190bbb516e1cc907689 |
completed | March 9, 2026, 7:10 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae733c44008190ac1429da66cf77aa |
completed | March 9, 2026, 7:14 a.m. |
Created at: March 4, 2026, 7:48 p.m.