Triple
T3270200
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Philip William, Prince of Orange |
E68627
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Filips
Filips is the given name of Philip William, Prince of Orange, a 16th-century Dutch nobleman and heir to William the Silent.
|
E342542
|
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: Filips | Statement: [Philip William, Prince of Orange, givenName, Filips]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Filips Context triple: [Philip William, Prince of Orange, givenName, Filips]
-
A.
Philipse
Philipse is the surname of a prominent colonial-era merchant and landowning family in what is now New York, notably associated with Frederick Philipse I.
-
B.
Phillips
Phillips is a common English-language surname borne by numerous notable individuals across fields such as science, politics, sports, and the arts.
-
C.
Herve
Herve is a municipality in the province of Liège in Wallonia, eastern Belgium, known for its rural landscape and traditional Herve cheese.
-
D.
Hugo Boss
Hugo Boss is a German luxury fashion house known for its high-end menswear, fragrances, and accessories.
-
E.
Valentino
Valentino is a renowned Italian luxury fashion house celebrated for its elegant haute couture, ready-to-wear, and iconic red-carpet designs.
- 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: Filips Triple: [Philip William, Prince of Orange, givenName, Filips]
Generated description
Filips is the given name of Philip William, Prince of Orange, a 16th-century Dutch nobleman and heir to William the Silent.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Filips Target entity description: Filips is the given name of Philip William, Prince of Orange, a 16th-century Dutch nobleman and heir to William the Silent.
-
A.
Philipse
Philipse is the surname of a prominent colonial-era merchant and landowning family in what is now New York, notably associated with Frederick Philipse I.
-
B.
Phillips
Phillips is a common English-language surname borne by numerous notable individuals across fields such as science, politics, sports, and the arts.
-
C.
Herve
Herve is a municipality in the province of Liège in Wallonia, eastern Belgium, known for its rural landscape and traditional Herve cheese.
-
D.
Hugo Boss
Hugo Boss is a German luxury fashion house known for its high-end menswear, fragrances, and accessories.
-
E.
Valentino
Valentino is a renowned Italian luxury fashion house celebrated for its elegant haute couture, ready-to-wear, and iconic red-carpet designs.
- 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_69ad859b54f881909bf530d549caf2fd |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adaff349148190beae8c0994b7ad83 |
completed | March 8, 2026, 5:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b28efded588190bd6c361e5298b496 |
completed | March 12, 2026, 10:01 a.m. |
| NEDg | Description generation | batch_69b28fcef05c8190b9141dcaa3f9145b |
completed | March 12, 2026, 10:05 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b2d6c47e908190b5be5b33da358ade |
completed | March 12, 2026, 3:07 p.m. |
Created at: March 8, 2026, 3:09 p.m.