Triple
T9149609
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bill Denbrough |
E219549
|
entity |
| Predicate | fights |
P30824
|
FINISHED |
| Object | It (entity) |
E37260
|
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: It (entity) | Statement: [Bill Denbrough, fights, It (entity)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: It (entity) Context triple: [Bill Denbrough, fights, It (entity)]
-
A.
Itu
Itu is a historic municipality in the interior of the Brazilian state of São Paulo, known for its colonial architecture and humorous reputation as the “city of exaggeration.”
-
B.
Itsa
Itsa is a town in Egypt located within the Faiyum Governorate, known primarily as a local agricultural and administrative center.
-
C.
In It What Is In It
"In It What Is In It" is the common English title of Rumi’s prose work *Fihi Ma Fihi*, a collection of his spiritual discourses and teachings.
-
D.
It
chosen
It is a 1986 horror novel by Stephen King about a shape-shifting entity that terrorizes children in the town of Derry, Maine.
-
E.
It
"It" is a 1927 silent romantic comedy film that made Clara Bow famous as the original "It Girl" and a major Hollywood star.
- 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_69ca83e121dc81909912bd66953081c5 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cca96908d88190a1d12517f6d1aece |
completed | April 1, 2026, 5:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0483cdd048190904260c44a5810cf |
completed | April 3, 2026, 11:07 p.m. |
Created at: March 30, 2026, 7:20 p.m.