Triple
T15553793
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hailey Bieber |
E370817
|
entity |
| Predicate | hasModeledFor |
P17880
|
FINISHED |
| Object | Guess |
E219173
|
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: Guess | Statement: [Hailey Bieber, hasModeledFor, Guess]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Guess Context triple: [Hailey Bieber, hasModeledFor, Guess]
-
A.
Guess
chosen
Guess is an American fashion brand known for its trendy denim, apparel, and accessories.
-
B.
Tenebak
Tenebak is a barangay (village-level administrative division) within the municipality of Maitum in the province of Sarangani, Philippines.
-
C.
Guess Kids
Guess Kids is the children's clothing and accessories line of the American fashion brand Guess, offering trendy apparel for younger customers.
-
D.
Guess Who
Guess Who is a song by the American rock band Alabama Shakes from their critically acclaimed album "Sound & Color."
-
E.
Guess Who
Guess Who is a 2005 comedy film loosely inspired by "Guess Who's Coming to Dinner," featuring Bernie Mac as an overprotective father meeting his daughter's white fiancé.
- 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_69d85cc6cf40819091f4a5facee1ebe6 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04a96c0c88190808f68601a36b506 |
completed | April 16, 2026, 2:33 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff456209288190aba6debd434af741 |
completed | May 9, 2026, 2:32 p.m. |
Created at: April 10, 2026, 4:09 a.m.