Triple
T9087140
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lily Atkinson |
E217786
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Lily |
E52031
|
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: Lily | Statement: [Lily Atkinson, givenName, Lily]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lily Context triple: [Lily Atkinson, givenName, Lily]
-
A.
Lily
chosen
Lily is a feminine given name of English origin commonly associated with the lily flower and symbolizing purity and beauty.
-
B.
Lily
Lily is a pivotal character in the psychological thriller film "Black Swan," serving as a seductive and enigmatic rival whose presence intensifies the protagonist's descent into paranoia and self-destruction.
-
C.
Lily
Lily is a woman romantically involved with Frank Money in Toni Morrison’s novel "Home."
-
D.
Lily
Lily is a fictional character from the British television sitcom "The Rag Trade," which humorously portrays the lives and conflicts of workers in a small clothing factory.
-
E.
Lily
Lily is the central protagonist of the film "Damsels in Distress," around whom the story’s events and character dynamics revolve.
- 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_69ca83d8ab5881909d8fddae363b32b1 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cc965646408190b041ab0e2d5dbc94 |
completed | April 1, 2026, 3:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d017c893fc819087d18db2383b9bb4 |
completed | April 3, 2026, 7:40 p.m. |
Created at: March 30, 2026, 7:13 p.m.