Triple
T1520460
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Morris Halle |
E32214
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Halle
Halle is a surname most notably borne by Morris Halle, a prominent linguist and phonologist.
|
E179074
|
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: Halle | Statement: [Morris Halle, familyName, Halle]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Halle Context triple: [Morris Halle, familyName, Halle]
-
A.
Halle (Saale)
Halle (Saale) is a major city in the German state of Saxony-Anhalt, known as an important economic, cultural, and educational center, including being home to the Martin Luther University of Halle-Wittenberg.
-
B.
Hanover
Hanover is a historic city in northern Germany that served as the capital of the former Kingdom of Hanover and the ancestral seat of the British House of Hanover.
-
C.
Hanover
Hanover is a small New Hampshire town best known as the home of Dartmouth College, an Ivy League institution.
-
D.
Hinckley
Hinckley is a market town in southwest Leicestershire, England, known historically for its hosiery industry and its location between Coventry and Leicester.
-
E.
Hilden
Hilden is a town in western Germany’s North Rhine-Westphalia region, known for its proximity to Düsseldorf and its mix of residential, commercial, and light industrial areas.
- 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: Halle Triple: [Morris Halle, familyName, Halle]
Generated description
Halle is a surname most notably borne by Morris Halle, a prominent linguist and phonologist.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Halle Target entity description: Halle is a surname most notably borne by Morris Halle, a prominent linguist and phonologist.
-
A.
Halle (Saale)
Halle (Saale) is a major city in the German state of Saxony-Anhalt, known as an important economic, cultural, and educational center, including being home to the Martin Luther University of Halle-Wittenberg.
-
B.
Hanover
Hanover is a historic city in northern Germany that served as the capital of the former Kingdom of Hanover and the ancestral seat of the British House of Hanover.
-
C.
Hanover
Hanover is a small New Hampshire town best known as the home of Dartmouth College, an Ivy League institution.
-
D.
Hinckley
Hinckley is a market town in southwest Leicestershire, England, known historically for its hosiery industry and its location between Coventry and Leicester.
-
E.
Hilden
Hilden is a town in western Germany’s North Rhine-Westphalia region, known for its proximity to Düsseldorf and its mix of residential, commercial, and light industrial areas.
- 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_69a885e9b0ac819093a9806ad0efc82c |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a907f071848190a5fb8fa1b97ef4de |
completed | March 5, 2026, 4:34 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad401616ec81908edd9dcb9f4a0184 |
completed | March 8, 2026, 9:23 a.m. |
| NEDg | Description generation | batch_69ad4130bf30819092be42a4e9225220 |
completed | March 8, 2026, 9:28 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ad41968e4c8190b843b97e18ac9968 |
completed | March 8, 2026, 9:29 a.m. |
Created at: March 4, 2026, 7:26 p.m.