Triple
T21353797
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bitche canton |
E526557
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Hinsingen
Hinsingen is a small commune in northeastern France, situated in the Moselle department within the historical region of Lorraine.
|
E1481569
|
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: Hinsingen | Statement: [Bitche canton, contains, Hinsingen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hinsingen Context triple: [Bitche canton, contains, Hinsingen]
-
A.
Menzingen
Menzingen is a municipality in the canton of Zug in central Switzerland, known for its rural landscape and location in the pre-Alpine region.
-
B.
Münsingen
Münsingen is a Swiss municipality in the canton of Bern, known for its scenic location in the Aare valley between Bern and Thun.
-
C.
Ehningen
Ehningen is a small German municipality in the state of Baden-Württemberg, located near Stuttgart and known for its mix of residential areas and technology-oriented businesses.
-
D.
Härkingen
Härkingen is a municipality in the canton of Solothurn in Switzerland, located in the Gäu district.
-
E.
Grosselfingen
Grosselfingen is a small municipality in the Zollernalb district of Baden-Württemberg in southwestern 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: Hinsingen Triple: [Bitche canton, contains, Hinsingen]
Generated description
Hinsingen is a small commune in northeastern France, situated in the Moselle department within the historical region of Lorraine.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hinsingen Target entity description: Hinsingen is a small commune in northeastern France, situated in the Moselle department within the historical region of Lorraine.
-
A.
Menzingen
Menzingen is a municipality in the canton of Zug in central Switzerland, known for its rural landscape and location in the pre-Alpine region.
-
B.
Münsingen
Münsingen is a Swiss municipality in the canton of Bern, known for its scenic location in the Aare valley between Bern and Thun.
-
C.
Ehningen
Ehningen is a small German municipality in the state of Baden-Württemberg, located near Stuttgart and known for its mix of residential areas and technology-oriented businesses.
-
D.
Härkingen
Härkingen is a municipality in the canton of Solothurn in Switzerland, located in the Gäu district.
-
E.
Grosselfingen
Grosselfingen is a small municipality in the Zollernalb district of Baden-Württemberg in southwestern 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_69e0b51cd5cc81909ac1187971e8a8ad |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69ee5bac9e1481909121e89f046c77f6 |
completed | April 26, 2026, 6:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a09b41898a48190afbc210866b3adcb |
completed | May 17, 2026, 12:27 p.m. |
| NEDg | Description generation | batch_6a09b4cc6f8081908b0ad1ce87b47e70 |
completed | May 17, 2026, 12:30 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a09b79237908190bf90b4316b58c7b1 |
completed | May 17, 2026, 12:41 p.m. |
Created at: April 16, 2026, 5:05 p.m.