Triple
T695570
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Christina Ricci |
E13887
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Christina |
E75185
|
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: Christina | Statement: [Christina Ricci, givenName, Christina]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Christina Context triple: [Christina Ricci, givenName, Christina]
-
A.
Christina
chosen
Christina is a feminine given name widely used in many cultures, often associated with notable figures in entertainment, arts, and public life.
-
B.
Christina Unkel
Christina Unkel is an American soccer referee and sports analyst known for officiating at high levels of the women's game, including major NWSL and international competitions.
-
C.
Paula
Paula is a feminine given name used in many languages, derived from the Latin name Paulus meaning "small" or "humble."
-
D.
Cynthia
Cynthia is a common feminine given name used in various cultures, often associated with the Greek moon goddess Artemis.
-
E.
Kristen
Kristen is a central female character in the romantic comedy film "Think Like a Man," whose love life and personal growth are explored through the movie’s ensemble relationship dynamics.
- 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_69a493406c408190957eeec9048a8fb6 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a0c5f51c8190acc4915099e4b384 |
completed | March 1, 2026, 8:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a7cf498af4819085d494f85adf0825 |
completed | March 4, 2026, 6:20 a.m. |
Created at: March 1, 2026, 7:36 p.m.