Triple

T30070955
Position Surface form Disambiguated ID Type / Status
Subject 100 Girls E764180 entity
Predicate characterPlayedBy Jonathan Tucker P195274 FINISHED
Object Matthew
Matthew is a fictional character from the teen comedy film "100 Girls," portrayed by actor Jonathan Tucker.
 E1898313 NE FINISHED

How this triple was built (3 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: Matthew | Statement: [100 Girls, characterPlayedBy Jonathan Tucker, Matthew]
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: Matthew
Triple: [100 Girls, characterPlayedBy Jonathan Tucker, Matthew]
Generated description
Matthew is a fictional character from the teen comedy film "100 Girls," portrayed by actor Jonathan Tucker.
PD Predicate disambiguation gpt-5-mini-2025-08-07 
Target predicate: characterPlayedBy Jonathan Tucker
Context triple: [100 Girls, characterPlayedBy Jonathan Tucker, Matthew]
  • A. characterPlayedBy Kenneth Connor
    Indicates that a character is portrayed or acted by Kenneth Connor.
  • B. characterPlayedByEdwardMulhare
    Indicates that the subject is a character that was portrayed or played by Edward Mulhare.
  • C. characterVoicedBy Seann William Scott
    Indicates that a character is voiced by Seann William Scott.
  • D. characterPlayedBy_Freddy Rodríguez
    Indicates that a given character is portrayed or played by the actor Freddy Rodríguez.
  • E. characterVoicedBy Denis Leary
    Indicates that the character is voiced by Denis Leary.
  • F. None of above. chosen

Provenance (7 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_69f2247221388190a13a22c47094a0ef completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69fdb45537288190b6791078d4a6899f completed May 8, 2026, 10 a.m.
NED1 Entity disambiguation (via context triple) batch_6a274c9e17bc8190ad45c636fce0d07a completed June 8, 2026, 11:13 p.m.
NEDg Description generation batch_6a274db4d6a88190a6c3cfbb05fa7320 completed June 8, 2026, 11:18 p.m.
NED2 Entity disambiguation (via description) batch_6a274e7037d48190869592da30780fc0 completed June 8, 2026, 11:21 p.m.
PD Predicate disambiguation batch_69fdb39ad96481908376d7def9fafc13 completed May 8, 2026, 9:57 a.m.
PDg Predicate description generation batch_69fdb4544b548190b8971f8055d48caa completed May 8, 2026, 10 a.m.
Created at: April 29, 2026, 7 p.m.