Triple

T9670677
Position Surface form Disambiguated ID Type / Status
Subject Armide E234017 entity
Predicate character P662 FINISHED
Object Phénice
Phénice is a secondary character in Jean-Baptiste Lully’s opera *Armide*, serving as one of Armide’s confidantes and attendants.
 E813283 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: Phénice | Statement: [Armide, character, Phénice]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Phénice
Context triple: [Armide, character, Phénice]
  • A. Naousa
    Naousa is a historic town in northern Greece known for its wine production, natural springs, and role in the Greek War of Independence.
  • B. Theodosia
    Theodosia is a feminine given name of Greek origin, historically associated with early Christian martyrs and later borne by notable women in American history.
  • C. Theodosia
    Theodosia is a historic port city on the southeastern coast of Crimea, known for its long history as a trading center on the Black Sea.
  • D. Sigeion
    Sigeion was an ancient Greek city in the Troad region near the entrance to the Hellespont, strategically important for controlling access to the Black Sea.
  • E. Phaestis
    Phaestis is known in some ancient biographical traditions as the mother of the Greek philosopher Aristotle.
  • 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: Phénice
Triple: [Armide, character, Phénice]
Generated description
Phénice is a secondary character in Jean-Baptiste Lully’s opera *Armide*, serving as one of Armide’s confidantes and attendants.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Phénice
Target entity description: Phénice is a secondary character in Jean-Baptiste Lully’s opera *Armide*, serving as one of Armide’s confidantes and attendants.
  • A. Naousa
    Naousa is a historic town in northern Greece known for its wine production, natural springs, and role in the Greek War of Independence.
  • B. Theodosia
    Theodosia is a feminine given name of Greek origin, historically associated with early Christian martyrs and later borne by notable women in American history.
  • C. Theodosia
    Theodosia is a historic port city on the southeastern coast of Crimea, known for its long history as a trading center on the Black Sea.
  • D. Sigeion
    Sigeion was an ancient Greek city in the Troad region near the entrance to the Hellespont, strategically important for controlling access to the Black Sea.
  • E. Phaestis
    Phaestis is known in some ancient biographical traditions as the mother of the Greek philosopher Aristotle.
  • 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_69ca848f55e48190b3f67252571c3d45 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd9c3ec17081908c2da74a1d1f49da completed April 1, 2026, 10:29 p.m.
NED1 Entity disambiguation (via context triple) batch_69d18a247ca48190910624dfbf0b491d completed April 4, 2026, 10:01 p.m.
NEDg Description generation batch_69d18acf86588190bc000f701bcaaa1c completed April 4, 2026, 10:03 p.m.
NED2 Entity disambiguation (via description) batch_69d18ba396cc8190a3ded2ac3968c553 completed April 4, 2026, 10:07 p.m.
Created at: March 30, 2026, 8:15 p.m.