Triple
T15964608
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anthony Powell |
E387151
|
entity |
| Predicate | academyAwardForBestCostumeDesignWonFor |
P37391
|
FINISHED |
| Object | Tess |
E265925
|
NE FINISHED |
How this triple was built (2 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: Tess | Statement: [Anthony Powell, academyAwardForBestCostumeDesignWonFor, Tess]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tess Context triple: [Anthony Powell, academyAwardForBestCostumeDesignWonFor, Tess]
-
A.
Tess
Tess is a central character in the musical film "Burlesque," serving as the tough but caring owner and manager of the struggling burlesque club.
-
B.
Tess
chosen
Tess is a 1979 period drama film directed by Roman Polanski, adapted from Thomas Hardy’s novel "Tess of the d'Urbervilles."
-
C.
Tess
Tess is a character in the action film "Fast X," part of the long-running Fast & Furious franchise.
-
D.
Tess
Tess is a central angelic character from the television series "Touched by an Angel," known for her wise, no-nonsense guidance to both humans and fellow angels.
-
E.
Tess
Tess is the given name of American poet, essayist, and short story writer Tess Gallagher.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d86da94ccc819083d187f5dc6a123e |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1828cd83c8190a3e15cccc8342c1f |
completed | April 17, 2026, 12:45 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffc3c5a7d48190891e69314e67e9af |
completed | May 9, 2026, 11:31 p.m. |
Created at: April 10, 2026, 4:54 a.m.