Triple
T3141472
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | canton of Mitry-Mory |
E65654
|
entity |
| Predicate | containsCommune |
P15149
|
FINISHED |
| Object |
Longperrier
Longperrier is a small French commune located in the Île-de-France region in north-central France.
|
E332689
|
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: Longperrier | Statement: [canton of Mitry-Mory, containsCommune, Longperrier]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Longperrier Context triple: [canton of Mitry-Mory, containsCommune, Longperrier]
-
A.
Sauvestre
Sauvestre is a French surname most notably associated with architect Stephen Sauvestre, who contributed to the design of the Eiffel Tower.
-
B.
Lemerig
Lemerig is an endangered Oceanic language spoken by a small community on the island of Vanua Lava in northern Vanuatu.
-
C.
Doncieux
Doncieux is a French surname most notably associated with Camille Doncieux, the first wife and frequent model of painter Claude Monet.
-
D.
Lacedelli
Lacedelli is an Italian surname most notably associated with Lino Lacedelli, one of the first climbers to reach the summit of K2.
-
E.
Nantz
Nantz is the surname of Jim Nantz, a prominent American sportscaster best known for his long-running work with CBS Sports covering events like the NFL, NCAA basketball, and The Masters.
- 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: Longperrier Triple: [canton of Mitry-Mory, containsCommune, Longperrier]
Generated description
Longperrier is a small French commune located in the Île-de-France region in north-central France.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Longperrier Target entity description: Longperrier is a small French commune located in the Île-de-France region in north-central France.
-
A.
Sauvestre
Sauvestre is a French surname most notably associated with architect Stephen Sauvestre, who contributed to the design of the Eiffel Tower.
-
B.
Lemerig
Lemerig is an endangered Oceanic language spoken by a small community on the island of Vanua Lava in northern Vanuatu.
-
C.
Doncieux
Doncieux is a French surname most notably associated with Camille Doncieux, the first wife and frequent model of painter Claude Monet.
-
D.
Lacedelli
Lacedelli is an Italian surname most notably associated with Lino Lacedelli, one of the first climbers to reach the summit of K2.
-
E.
Nantz
Nantz is the surname of Jim Nantz, a prominent American sportscaster best known for his long-running work with CBS Sports covering events like the NFL, NCAA basketball, and The Masters.
- 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_69ad8582f564819088c27e1f96153938 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada57895dc8190bd3d4ef9391973dc |
completed | March 8, 2026, 4:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b224e4df38819089d0a11016a85ad8 |
completed | March 12, 2026, 2:28 a.m. |
| NEDg | Description generation | batch_69b228c1b568819088dc5ce4a15fedc2 |
completed | March 12, 2026, 2:45 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b22ccd34a8819089e207b6ee1f634a |
completed | March 12, 2026, 3:02 a.m. |
Created at: March 8, 2026, 3:05 p.m.