Triple
T9820220
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stephen McHattie |
E238509
|
entity |
| Predicate | appearedIn |
P795
|
FINISHED |
| Object |
Haven
Haven is a supernatural mystery television series set in a small Maine town plagued by strange, unexplained phenomena.
|
E823812
|
NE FINISHED |
How this triple was built (4 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: Haven | Statement: [Stephen McHattie, appearedIn, Haven]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Haven Context triple: [Stephen McHattie, appearedIn, Haven]
-
A.
Haven
"Haven" is a literary work by British writer and socialite Elizabeth Asquith, reflecting her early 20th-century intellectual and artistic milieu.
-
B.
Havens
Havens is the surname of Richie Havens, the American folk singer and guitarist best known for his iconic opening performance at the 1969 Woodstock Festival.
-
C.
Moonhaven
Moonhaven is a science fiction television series set in a utopian lunar colony that becomes central to humanity’s survival.
-
D.
Stars Hollow
Stars Hollow is the quirky, close-knit small-town setting of the television series "Gilmore Girls," known for its eccentric residents and charming New England atmosphere.
-
E.
Harborland
Harborland is a popular waterfront shopping and entertainment district in Kobe, Japan, known for its modern malls, restaurants, and scenic harbor views.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Haven Triple: [Stephen McHattie, appearedIn, Haven]
Generated description
Haven is a supernatural mystery television series set in a small Maine town plagued by strange, unexplained phenomena.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Haven Target entity description: Haven is a supernatural mystery television series set in a small Maine town plagued by strange, unexplained phenomena.
-
A.
Haven
"Haven" is a literary work by British writer and socialite Elizabeth Asquith, reflecting her early 20th-century intellectual and artistic milieu.
-
B.
Havens
Havens is the surname of Richie Havens, the American folk singer and guitarist best known for his iconic opening performance at the 1969 Woodstock Festival.
-
C.
Moonhaven
Moonhaven is a science fiction television series set in a utopian lunar colony that becomes central to humanity’s survival.
-
D.
Stars Hollow
Stars Hollow is the quirky, close-knit small-town setting of the television series "Gilmore Girls," known for its eccentric residents and charming New England atmosphere.
-
E.
Harborland
Harborland is a popular waterfront shopping and entertainment district in Kobe, Japan, known for its modern malls, restaurants, and scenic harbor views.
- F. None of above. chosen
Provenance (5 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_69ca84dfde1481909f47c286d715f892 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb313134081908eb0ba3a22b22e2b |
completed | April 2, 2026, 12:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1cc78ffcc8190bb26a224350376dc |
completed | April 5, 2026, 2:44 a.m. |
| NEDg | Description generation | batch_69d1cd8e7c548190bc3f10004db80925 |
completed | April 5, 2026, 2:48 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d1ce1aead081908da4a85ded350c17 |
completed | April 5, 2026, 2:51 a.m. |
Created at: March 30, 2026, 8:31 p.m.