Triple
T12464660
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Castrogiovanni |
E297890
|
entity |
| Predicate | hasAlternativeName |
P39
|
FINISHED |
| Object | Henna |
E924723
|
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: Henna | Statement: [Castrogiovanni, hasAlternativeName, Henna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Henna Context triple: [Castrogiovanni, hasAlternativeName, Henna]
-
A.
Henna
chosen
Henna is a 1991 Indian romantic drama film directed by Randhir Kapoor, known for its cross-border love story set between India and Pakistan.
-
B.
Tinte
Tinte is a small village in the Dutch province of South Holland, known for its rural character and annual local festivities.
-
C.
Surma
Surma is a group of closely related languages spoken by indigenous communities in southwestern Ethiopia and neighboring regions.
-
D.
War Paint
War Paint is a Broadway musical that dramatizes the rivalry between cosmetics titans Helena Rubinstein and Elizabeth Arden in mid-20th-century America.
-
E.
Neela
Neela is a prominent commander in the monkey kingdom of Kishkindha in the Indian epic Ramayana, known for his leadership in Rama’s campaign against Ravana.
- 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_69d6ada270808190b1a2b2e7b02bb426 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94db7828481909d8f02b2fde83567 |
completed | April 10, 2026, 7:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f63f1f44f481909c7efdffd2aeac41 |
completed | May 2, 2026, 6:14 p.m. |
Created at: April 8, 2026, 9:56 p.m.