Triple
T17813465
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Metz Métropole |
E444773
|
entity |
| Predicate | containsMunicipality |
P852
|
FINISHED |
| Object |
Lessy
Lessy is a small French commune located in the Moselle department in northeastern France, near the city of Metz.
|
E1289701
|
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: Lessy | Statement: [Metz Métropole, containsMunicipality, Lessy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lessy Context triple: [Metz Métropole, containsMunicipality, Lessy]
-
A.
Lessa
Lessa is the fiercely determined and telepathically gifted heroine of Anne McCaffrey’s Dragonriders of Pern series, known for her bond with the golden queen dragon Ramoth and her pivotal role in saving Pern.
-
B.
Lessi
Lessi is a diminutive or affectionate nickname commonly used for the Italian given name Alessandro.
-
C.
Lark
Lark is a cloud-based workplace collaboration and productivity platform offering integrated messaging, video conferencing, calendars, and document tools, developed under ByteDance.
-
D.
Lark
Lark was a famous overnight passenger train that ran between San Francisco and Los Angeles, known for its streamlined design and sleeper service.
-
E.
Lark
Lark is a brand of cigarettes historically marketed by Liggett & Myers and known for its distinctive charcoal filter.
- 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: Lessy Triple: [Metz Métropole, containsMunicipality, Lessy]
Generated description
Lessy is a small French commune located in the Moselle department in northeastern France, near the city of Metz.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lessy Target entity description: Lessy is a small French commune located in the Moselle department in northeastern France, near the city of Metz.
-
A.
Lessa
Lessa is the fiercely determined and telepathically gifted heroine of Anne McCaffrey’s Dragonriders of Pern series, known for her bond with the golden queen dragon Ramoth and her pivotal role in saving Pern.
-
B.
Lessi
Lessi is a diminutive or affectionate nickname commonly used for the Italian given name Alessandro.
-
C.
Lark
Lark is a cloud-based workplace collaboration and productivity platform offering integrated messaging, video conferencing, calendars, and document tools, developed under ByteDance.
-
D.
Lark
Lark was a famous overnight passenger train that ran between San Francisco and Los Angeles, known for its streamlined design and sleeper service.
-
E.
Lark
Lark is a brand of cigarettes historically marketed by Liggett & Myers and known for its distinctive charcoal filter.
- 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_69d8b9f0de78819099395b14db75a8a6 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e4887d6828819085face29cb03671b |
completed | April 19, 2026, 7:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a02ff6254308190837052cba58d45c8 |
completed | May 12, 2026, 10:22 a.m. |
| NEDg | Description generation | batch_6a0300aa8f2c8190adb9ed9ec63a9f9a |
completed | May 12, 2026, 10:27 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0301570f188190a330b2a9f5f45191 |
completed | May 12, 2026, 10:30 a.m. |
Created at: April 10, 2026, 10:14 a.m.