Triple
T30059209
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maureen Starkey Tigrett |
E763822
|
entity |
| Predicate | hairdresserFor |
P146352
|
FINISHED |
| Object | The Beatles (occasionally) |
—
|
NE NERFINISHED |
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: The Beatles (occasionally) | Statement: [Maureen Starkey Tigrett, hairdresserFor, The Beatles (occasionally)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hairdresserFor Context triple: [Maureen Starkey Tigrett, hairdresserFor, The Beatles (occasionally)]
-
A.
hairCraftedBy
Indicates that a hairstyle or hair-related work was created or styled by a specific person or agent.
-
B.
hairdressingClientele
chosen
Indicates that one entity serves as the customer or client receiving hairdressing services from another entity.
-
C.
hairSpecialty
Indicates a professional focus or expertise in working with a particular type, style, or treatment of hair.
-
D.
stylist
Indicates that one entity serves as a stylist for another, providing professional advice or services related to appearance, fashion, or design.
-
E.
haircuttingSkill
Indicates the degree to which one entity is capable of effectively cutting another entity’s hair.
- F. None of above.
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_69f224716378819087a722e487832b70 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f67ca2136881909b54de5f078579d9 |
completed | May 2, 2026, 10:37 p.m. |
| PD | Predicate disambiguation | batch_69f673c664f08190b4d66cdc305e10db |
completed | May 2, 2026, 9:59 p.m. |
Created at: April 29, 2026, 6:57 p.m.