Triple
T1652324
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Göppingen |
E35718
|
entity |
| Predicate | locatedOnRiver |
P165
|
FINISHED |
| Object |
Fils
The Fils is a river in the German state of Baden-Württemberg that flows through towns such as Göppingen before joining the Neckar.
|
E186662
|
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: Fils | Statement: [Göppingen, locatedOnRiver, Fils]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fils Context triple: [Göppingen, locatedOnRiver, Fils]
-
A.
Clément
Clément is a French given name, equivalent to Clement in English, commonly used for males.
-
B.
Jacques
Jacques is the French form of the given name James, commonly used in French-speaking countries.
-
C.
Théodore
Théodore is a masculine given name of Greek origin, commonly used in French-speaking countries and borne by notable figures such as the Reformation theologian Théodore Beza.
-
D.
Guillaume
Guillaume is the French form of the given name William, commonly used in French-speaking countries.
-
E.
Michel
Michel is the birth name of the acclaimed Egyptian actor Omar Sharif, renowned for his roles in classic films such as "Lawrence of Arabia" and "Doctor Zhivago."
- 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: Fils Triple: [Göppingen, locatedOnRiver, Fils]
Generated description
The Fils is a river in the German state of Baden-Württemberg that flows through towns such as Göppingen before joining the Neckar.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Fils Target entity description: The Fils is a river in the German state of Baden-Württemberg that flows through towns such as Göppingen before joining the Neckar.
-
A.
Clément
Clément is a French given name, equivalent to Clement in English, commonly used for males.
-
B.
Jacques
Jacques is the French form of the given name James, commonly used in French-speaking countries.
-
C.
Théodore
Théodore is a masculine given name of Greek origin, commonly used in French-speaking countries and borne by notable figures such as the Reformation theologian Théodore Beza.
-
D.
Guillaume
Guillaume is the French form of the given name William, commonly used in French-speaking countries.
-
E.
Michel
Michel is the birth name of the acclaimed Egyptian actor Omar Sharif, renowned for his roles in classic films such as "Lawrence of Arabia" and "Doctor Zhivago."
- 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_69a8860568888190a32cd9f70acbba42 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a90a88cb108190a836b972f600c257 |
completed | March 5, 2026, 4:46 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad60ac133881909222b25029096407 |
completed | March 8, 2026, 11:42 a.m. |
| NEDg | Description generation | batch_69ad613d218081909e3ed7e74dcf82fc |
completed | March 8, 2026, 11:45 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ad61b178148190a088495eb599c9cb |
completed | March 8, 2026, 11:46 a.m. |
Created at: March 4, 2026, 7:29 p.m.