Triple
T36413179
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Janus Thickey Ward |
E896935
|
entity |
| Predicate | hasVisitingCharacters |
P182462
|
FINISHED |
| Object | Harry Potter |
—
|
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: Harry Potter | Statement: [Janus Thickey Ward, hasVisitingCharacters, Harry Potter]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasVisitingCharacters Context triple: [Janus Thickey Ward, hasVisitingCharacters, Harry Potter]
-
A.
hasGuestCharacter
chosen
Indicates that an entity features another entity in a temporary or special guest role, rather than as a regular or primary participant.
-
B.
visitedByCharacter
Indicates that a location or place is visited or physically gone to by a specific character.
-
C.
hasCharacters
Indicates that an entity (such as a work or story) includes or features certain characters as part of its content.
-
D.
hasHumanCharacters
Indicates that the subject includes or features characters that are human beings.
-
E.
hasMainCharacterFrom
Indicates that a work of fiction has a main character who originates from or belongs to a specified place, group, or source.
- 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_69f76e54ce408190849acc3f7758937c |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69fd6dbd1b648190b1a0b391c03aebc5 |
completed | May 8, 2026, 4:59 a.m. |
| PD | Predicate disambiguation | batch_69fd6a9020548190bbfa845360ac85fb |
completed | May 8, 2026, 4:46 a.m. |
Created at: May 3, 2026, 4:10 p.m.